DETAILED SCIENTIFIC PROGRAM
(speakers are indicated in italics and underlined )
July 5, 2012 - 9:45 – 10:45 Keynote lecture (Auditorium)
Jerry Hausman (MIT)
“Estimating a Semi-Parametric Duration Model without Specifying Heterogeneity”
Session Chair : Jacques Mairesse, ENSAE and Maastricht University
July 5, 2012 - 11:15 – 13:00; Sessions A1 to A6
Session A1 – Production factors (Salle / Room 2)
Session Chair: Jacques Mairesse (CREST-ENSAE and Maastricht University)
Energy Consumption and Economic Growth: Evidence from Nonlinear Panel Cointegration and Causality Tests
Mübariz Hasanov (Hacettepe University); Tolga Omay (Cankaya University); Nuri Ucar (Hacettepe University)
Product and labor market imperfections and scale economies: Micro-evidence on France, Japan and the
Netherlands
Sabien Dobbelaere (VU University Amsterdam); Kozo Kiyota (Yokohama National University);
Jacques Mairesse (ENSAE and Maastricht University)
Capital Utilisation and Retirement
Antoine Bonleu (Université de la Méditerranée); Gilbert CETTE (Banque de France); Guillaume Horny (Banque
de France)
Session A2 – International Economics (Salle / Room 3)
Session Chair: Marcel Voia (Carleton University)
Real exchange rate and productivity in an OLG model
Thi Hong Thinh Doan (Université de la Méditerranée); Karine Gente (Université de la Méditerranée)
Does migration foster African exports?
Hélène Ehrhart (Banque de France); Maëlan Le Goff (CEPII); Emmanuel Rocher (Banque de France); Raju Singh
(World Bank)
Shifting Motives: Explaining the Buildup in Official Reserves in EMEs Since the 1980s
Atish Ghosh (International Monetary Fund); Jonathan Ostry (International Monetary Fund); Charalambos
Tsangarides (International Monetary Fund)
Currency Crises, Exchange Rate Regimes, and Capital Account Liberalization: A Duration Analysis Approach
Mohammad Karimi (University of Ottawa); Marcel Voia (Carleton University)
Session A3 – Multidimentional Fixed Effects (Salle / Room 4)
Session Chair: Laszlo Matyas (Central European University)
Linear Regression for Panel with Unknown Number of Factors as Interactive Fixed Effects
Hyungsik Roger Moon (University of Southern California); Martin Weidner (University College London)
The Estimation of Multi-dimensional Fixed Effects Panel Data Models
Laszlo Matyas (Central European University); Laszlo Balazsi (Central European University)
Session A4 – Dynamic Models 1 (Auditorium)
Session Chair: Lynda Khalaf (Carleton University)
Semiparametrically Efficient High-Dimensional GMM Estimator with Many Invalid Moment Conditions: An
Application to Dynamic Panel Data Models
Mehmet Caner (North Carolina State University); Xu Han (North Carolina State University); Yoonseok Lee
(University of Michigan)
Nonparametric Dynamic Panel Data Models: Kernel Estimation and Specification Testing
Xun Lu (Hong Kong University of Science and Technology); Liangjun Su (Singapore Management University)
Accuracy and efficiency of various GMM inference techniques in dynamic micro panel data models
Jan Kiviet (University of Amsterdam); Milan Pleus (University of Amsterdam); Rutger Poldermans (University of
Amsterdam)
Aggregation in Large Dynamic Panels
Hashem Pesaran (University of Cambridge and USC); Alexander Chudik (Federal Reserve Bank of Dallas and
CIMF)
Session A5 – Non-Linear Models 1 (Salle / Room 1)
Session Chair: Mark Harris (Curtin University of Technology)
Simple estimators for count data models with sample selection
Koen Jochmans (Sciences Po)
A negative binomial model and moment conditions for count panel data
Yoshitsugu Kitazawa (Kyushu Sangyo University)
Nonparametric Quasi-Differencing with Applications
Kirill Evdokimov (Princeton University)
Location choice of immigrants in Belgium 1990-2007
Hubert Jayet (Université des Sciences et Technologies de Lille); Glenn Rayp (Ugent); Ilse Ruyssen (Ghent
University); Nadiya Ukrayinchuk (Université Lille 2)
Session A6 – Duration and survival models (Salle / Room 5)
Session Chair: Thierry Kamionka CREST-ENSAE, CNRS)
The Determinants of Firm Exit in the French Food Industries
Pierre Blanchard (UPEC, ERUDITE-TEPP); Jean-Pierre Huiban (INRA ALISS et UPEC, ERUDITE-TEPP);
Claude Mathieu (UPEC, ERUDITE-TEPP)
Does Tort Law Improve the Health of Newborns, or Miscarry? A Longitudinal Analysis of the Effect of Liability
Pressure on Birth Outcomes
Michelle M. Mello (Harvard School of Public Health); David M. Studdert (University of Melbourne); S.V.
Subramanian (Harvard School of Public Health); Y. Tony Yang (George Mason University )
The impact of health events on individual labor market histories: the message from difference in differences with
exact matching
Emmanuel Duguet (ERUDITE, Université Paris Est-Créteil); Christine LE CLAINCHE (Centre d’Études de
l’Emploi)
July 5, 2012 - 14:00 – 16:00; Sessions B1 to B6
Session B1 – Invited Session (Auditorium)
F. Palm and J.P. Urbain (Maastricht University):
Common and Idiosyncratic Features of Micro- and Macro-Panel Econometrics: On Issues and Findings on
Macro-Panels that could be of Interest for Macro-Panels”
Session Chair: H. Pesaran (Cambridge University and USC)
Session B2 – Dynamic Models 2 (Salle / Room 1)
Session Chair: Tom Wansbeek (University of Groningen)
Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models
Kazuhiko Hayakawa (Hiroshima University); Hashem Pesaran (University of Cambridge and USC)
GMM Estimation of short dynamic panel data models with cross-sectional dependence
Vasilis Sarafidis (University of Sydney)
Pairwise difference estimation of dynamic panel data models
Michele Aquaro (Tilburg University); Pavel Cizek (Tilburg University)
Semiparametric Estimation of Partially Varying-Coefficient Dynamic Panel Data Models
Zongwu Cai (University of North Carolina at Charlotte); Linna Chen (Xiamen University); Ying Fang (Xiamen
University)
Dynamic Panels with MIDAS Covariates: Estimation and Fit
Lynda Khalaf (Carleton University); Maral Kichian (Bank of Canada); Charles Saunders (Carleton University);
Marcel Voia (Carleton University)
Session B3 - Production and Productivity 1 (Salle / Room 3)
Session Chair: Almas Heshmati (Korea University)
Impact of Firing Restrictions on Firm Performance: Evidence from Indonesia
Peter Brummund (Cornell University)
The Impact of Capital Measurement Error Correction on Firm-Level Production Function Estimation
Kamil Galuscak (Czech National Bank); Lubomir Lizal (CERGE-EI)
Estimation of a Panel Stochastic Frontier Model with Unobserved Common Shocks
Chih-Chiang Hsu (National Central University); Chang-Ching Lin (Academia Sinica); Shou-Yung Yin (National
Central University)
Estimation and Efficiency Measurement in Stochastic Production Frontiers with Ordinal Outcomes
William Griffiths (University of Melbourne); Xiaohui Zhang (Monash University); Xueyan Zhao (Monash
University)
A stochastic frontier model with short-run and long-run inefficiency
Roberto Colombi (Università di Bergamo); Subal Kumbhakar (SUNY Binghamton and University of Stavanger,
Norway); Gianmaria Martini (Università di Bergamo); Giorgio Vittadini (CRISP, Università di Milano-Bicocca)
Session B4 - International Trade (Salle / Room 4)
Session Chair: Claude Mathieu (ERUDITE, Université Paris-Est Créteil)
The Hausman-Taylor Estimators on Three-level Data: An Application to Gravity Models of International Trade
Juyoung Cheong (University of Queensland); Do won Kwak (The University of Queensland); Kam Tang (School
of Economics)
The Elusive Impact of GATT/WTO Membership on International Trade
Laszlo Konya (La Trobe University); Laszlo Matyas (Central European University); Mark Harris (Curtin
University of Technology)
Outward Foreign Direct Investment and domestic performance: in search of a causal link
Emmanuel Dhyne (National Bank of Belgium); Selen Guerin (Vrije Universiteit Brussel)
The Growth and Volatility of French Exporters
Antoine Berthou (Banque de France); Vincent Vicard (Banque de France); Jean-Charles Bricongne (Banque de
France)
R&D Expenditures and the Global Diversification of Export Sales
Christopher Baum (Boston College); Mustafa Caglayan (University of Sheffield); Oleksandr Talavera (Durham
University)
Session B5 - Heterogeneity, Inequalities (Salle / Room 5)
Session Chair: Jaya Krishnakumar (University of Geneva)
Economic Cost of Gender Gaps: Africa’s Missing Growth Reserve
Amarakoon Bandara Amarakoon (United Nations Development Programme)
The Trend over Time of the Gender Wage Gap in Italy
Chiara Mussida (Catholic University of the Sacred Heart); Matteo Picchio (Tilburg University)
Glass Ceilings or Glass Doors? The Role of Firms in Male-Female Wage Disparities
Mohsen Javdani (Simon Fraser University)
Rising Inequality: transitory or permanent? New Evidence from a Panel of U.S. Tax Returns 1987-2006
Jason DeBacker (U.S. Department of the Treasury); Bradley Heim (Indiana University); Vasia Panousi (Federal
Reserve Board); Ivan Vidangos (Federal Reserve Board)
To what extent are US regional incomes converging?
Mark Holmes (Waikato University); Jesus Otero (Universidad del Rosario); Theodore Panagiotidis (University of
Macedonia)
Session B6 - Banking (Salle / Room 2)
Session Chair: Sanvi Avouyi-Dovi (Banque de France)
Collateralization, Bank Loan Rates and Monitoring: Evidence from a Natural Experiment
Geraldo Cerqueiro (Universidade Católica Portuguesa); Steven Ongena (Tilburg University); Kasper Roszbach
(Sveriges Riksbank and University of Groningen)
Cost of funds, credit risk and bank loan interest rates in the crisis. What do micro data tell us?
Sanvi Avouyi-Dovi (Banque de France); Guillaume Horny (Banque de France); Patrick Sevestre (Banque de
France and Paris School of Economics)
Credit Ratings and Bank Monitoring Ability
Leonard Nakamura (Federal Reserve Bank of Philadelphia); Kasper Roszbach (Sveriges Riksbank and University
of Groningen)
Rescue packages and bank lending
Michael Brei (EconomiX, Université Paris Ouest); Leonardo Gambacorta (Bank for International Settlements);
Goetz von Peter (Bank for International Settlements)
Bank leverage shocks and the macroeconomy: a new look in a data-rich environment
Jean-Stéphane Mesonnier (Banque de France); Dalibor Stevanovic (Université du Québec à Montréal)
July 5, 2012 - 16:30 - 18:30; Sessions C1 to C6
Session C1 - Econometric Theory 1 (Auditorium)
Session Chair: Badi Baltagi ( Syracuse University)
A Heteroskedasticity Robust Breusch-Pagan Test for Contemporaneous Correlation in Dynamic Panel Data
Models
Andreea Halunga (University of Exeter); Chris Orme (University of Manchester); Takashi Yamagata (University
of York)
A Consistent Nonparametric Test of Parametric Regression Functional Form in Fixed Effects Panel Data Models
Qi Li (Texas A&M University); Yiguo Sun (University of Guelph)
LM-type tests for slope homogeneity in panel data models
Christoph Roling (Bonn Graduate School of Economics); Joerg Breitung (University of Bonn); Nazarii Salish
(University of Bonn)
Analyzing Treatment Effects on Distributions with Complex Structure
Marcel Voia (Carleton University); Mark Bebbington (Massey University); Christopher Bennett (Vanderbilt
University); Ricardas Zitikis (The University of Western Ontario)
Session C2 - Human Capital, Employment and Wages (Salle / Room 4)
Session Chair: Denis Fougere, (ENSAE-CREST and Banque de France)
Do government purchases affect unemployment?
Steinar Holden (University of Oslo); Victoria Sparrman (Statistics Norway)
The Design of Unemployment Transfers: Evidence from a Dynamic Structural Life-cycle Model
Peter Haan (Deutsches Institut für Wirtschaftsforschung, DIW); Victoria Prowse (University of Oxford)
Youth labour market histories, neighborhood of origin and diploma: a dynamic modeling
Thierry Kamionka (CNRS); Xavier Vu Ngoc (Ecole Polytechnique)
Financial incentives and study duration in Higher Education
Trude Gunnes (Statistics Norway); Lars J. Kirkebøen (Statistics Norway); Marte Rønning (Statistics Norway)
Human capital investments and the life cycle variance of earnings
Thierry Magnac (University Toulouse 1 Capitole); Nicolas Pistolesi (Toulouse School of Economics
(GREMAQ)); Sébastien Roux (CREST/DARES)
Session C3 - Household Finance (Salle / Room 5)
Session Chair: Muriel Roger (INRA and Banque de France)
The Free Installment Puzzle
Sungjin Cho (Seoul National University); John Rust (University of Maryland)
An alternative mode of dealing with personal bankruptcy: the French household over-indebtedness commissions’
experience
Henri Fraisse (Banque de France); Philippe Frouté (Université de Paris-Est Créteil)
A Panel Latent Class Tobit Model:An application to Modelling Charitable Donations
Sarah Brown (University of Sheffield); William H. Greene (New York University); Mark Harris (Curtin
University of Technology); Karl Taylor (University of Sheffield)
Session C4 - R&D, Innovation and Productivity (Salle / Room 2)
Session Chair: Gilbert Cette (Banque de France)
Innovation and Welfare: Results from joint estimation of production and demand functions
Jordi Jaumandreu (Boston University) ; Jacques Mairesse (CREST-ENSAE and Maastricht University)
Productivity in China's High Technology Industry: Regional Heterogeneity and R&D
Rui Zhang (Sichuan University); Kai Sun (Aston University); Michael S. Delgado (Binghamton University); Subal
C. Kumbhakar (Binghamton University)
Spillovers and Strategic Dynamics in Product Innovation: Empirical Evidence from Japanese National Innovation
Survey 2009 and Implications for Public Financial Support
Daiya Isogawa (University of Tokyo); Hiroshi Ohashi (University of Tokyo)
The Size Does Matter: New Evidence on the Effect of Intellectual Property Rights on Innovation
Alexandru Minea (Université d’Auvergne)
How important is innovation? A Bayesian factor-augmented productivity model on panel data
Georges Bresson (Université Paris II / Sorbonne Université); Jean-Michel Etienne (Universite Paris-Sud 11);
Pierre Mohnen (Maastricht University)
Session C5 - Cross-section Dependence and Interactions (Salle / Room 1)
Session Chair: Georges Bresson (University of Paris II / Sorbonne Universities)
A Nonlinear Panel Unit Root Test under Cross Section Dependence
Mario Cerrato (University of Glasgow); Christian de Peretti (University of Lyon 1); Rolf Larsson (Uppsala
University); Nicholas Sarantis (London Metropolitan University)
Exponent of Cross-sectional Dependence: Estimation and Inference
Natalia Bailey (University of Cambridge); George Kapetanios (Queen Mary, University of London); Hashem
Pesaran (University of Cambridge and USC)
A Nonlinear Panel Data Model of Cross-Sectional Dependence
George Kapetanios (Queen Mary, University of London); James Mitchell (Niesr); Yongcheol Shin (University of
York)
Cross Section Dependence in Panel Data Efficiency Models for Technological Convergence
Camilla Mastromarco (University of Salento); Laura Serlenga (University of Bari); Yongcheol Shin (University of
York)
Testing for Cross-Sectional Dependence in a Panel Factor Model Using the Wild Bootstrap F-Test
Badi Baltagi (Syracuse University); Chihwa Kao (Syracuse University); Sanggon Na (Syracuse University)
Session C6 - Quantile Regression (Salle / Room 3)
Session Chair: Bo Honore (Princeton University)
Is there income convergence between Latin America and East Asia? An investigation using quantile regression
Geovana Bertussi (Universidade de Brasília - UnB); Lízia de Figueiredo (UFMG)
Semiparametric Quantile Panel Data Models with An Application to Estimating the Growth Effect of FDI
Zongwu Cai (University of North Carolina at Charlotte); Linna Chen (Xiamen University); Ying Fang (Xiamen
University)
Nonparametric Identification in Panels using Quantiles
Ivan Fernandez-Val (Boston University); Stefan Hoderlein (Boston College); Hajo Holzmann (Marburg
University); Whitney Newey (MIT)
Quantile Regression Estimation of Panel Duration Models with Censored Data
Matthew Harding (Stanford University); Carlos Lamarche (University of Oklahoma)
Average and Quantile Effects in Nonseparable Panel Models
Victor Chernozhukov (MIT); Ivan Fernandez-Val (Boston University); Jinyong Hahn (UCLA); Whitney Newey
(MIT)
July 6, 2012 - 9:15 - 10:15 Keynote Lecture (Auditorium)
C. Gourieroux (University of Toronto and CREST)
“Efficiency in Large Dynamic Panel Models with Common Factors”
Session Chair: Whitney Newey (MIT)
July 6, 2012 - 10:45 - 12:30; Sessions D1 to D6
Session D1 - Factor Models (Auditorium)
Session Chair: Franz Palm (Maastricht University)
Evaluating factor pricing models using high frequency panels
Yoosoon Chang (Indiana University); Hwagyun Kim (Texas A&M University); Joon Park (Indiana University)
A Stochastic Discount Factor Approach to Asset pricing using panel data asymptotics
Fabio Araújo (Princeton University); Joao Victor Issler (Getulio Vargas Foundation)
Efficient Estimation of Nonstationary Factor Models
In Choi (Sogang University)
Session D2 - Non-Linear Models 2 (Salle / Room 1)
Session Chair: Thierry Magnac ( University Toulouse 1 Capitole)
Unified estimation of panel regression models with simultaneous spatial and dynamic disturbances
Lung-fei Lee (Ohio State University); Jihai Yu (Peking University)
Binary Response Models for Repeated Cross Sections
David Pacini (University of Bristol)
Estimation of some nonlinear panel data models with both time-varying and time-invariant explanatory variables
Bo Honore (Princeton University); Michaela Kesina (ETH Zurich)
Variable coefficient binary choice panel data models: comparing Pooled and Mean Group estimators
Jaya Krishnakumar (University of Geneva); Laurent Pauwels (University of Sydney)
Session D3 – Prices (Salle / Room 2)
Session Chair: Hervé Le Bihan (Banque de France)
The Geography of Consumer Prices
Attila Ratfai (Central European University); Adam Reiff (National Bank of Hungary)
The Dynamics of Gasoline Prices: Evidence from Daily French Micro Data
Erwan Gautier (Université de Nantes); Ronan Le Saout (ENSAE)
Not an average story: Asymmetric price transmission in the Hungarian gasoline retail market
Gábor Koltay (European Commission)
What's up? Patterns of Microeconomic Price Adjustment in France Before and During the Crisis
Nicoletta Berardi (Banque de France); Erwan Gautier (Université de Nantes); Hervé Le Bihan (Banque de
France)
Session D4 - Time Series Panels (Salle / Room 3)
Session Chair: Esfandiar Maasoumi (Emory University)
Factor Augmented Autoregressive Distributed Lag Models
Serena Ng (Columbia University); Dalibor Stevanovic (Université du Québec à Montréal)
Bias Reduction under Dependence, in a Nonlinear and Dynamic Panel Setting: The Case of GARCH Panels
Cavit Pakel (University of Oxford)
IV-Based Cointegration Testing in Dependent Panels with Time-Varying Variance
Matei Demetrescu (University of Bonn); Christoph Hanck (Rijksuniversiteit Groningen); Adina Tarcolea (Goethe
University Frankfurt)
On the Applicability of the Sieve Bootstrap in Time Series Panels
Stephan Smeekes (Maastricht University); Jean-Pierre Urbain (Maastricht University)
Session D5 - Econometric Theory 2 (Salle / Room 4)
Session Chair: C. Gourieroux (University of Toronto and CREST)
Nonparametric estimation of finite mixtures
Stephane Bonhomme (CEMFI); Koen Jochmans (Science-Po); Jean-Marc Robin (Sciences-Po)
A strategy to reduce the count of moment conditions in panel data GMM
Maria Elena Bontempi (Università degli Studi di Bologna); Irene Mammi (University of Bologna)
Efficient GMM Estimation with a General Missing Data Pattern
Chris Muris (Simon Fraser University)
A Robust Hausman-Taylor Estimator
Badi Baltagi (Syracuse University); Georges Bresson (University of Paris II / Sorbonne Universities)
Session D6 – Forecasting (Salle / Room 5)
Session Chair: Pierre Sicsic ( Banque de France)
Microdata Imputations and Macrodata Implications: Evidence from the Ifo Business Survey
Christian Seiler (Ifo Institute); Christian Heumann (University of Munich)
Prediction in an Unbalanced Nested Error Component Panel Data Model
Badi Baltagi (Syracuse University); Alain Pirotte (University of Paris II)
Banking Crises, Early Warning Models, and Effciency
Pavlos Almanidis (Ernst & Young-Toronto); Robin Sickles (Rice University)
July 6, 2012 - 14:00 - 16:00; Sessions E1 to E6
Session E1 - Dynamic Models 3 (Salle / Room 4)
Session Chair: Patrick Sevestre (Banque de France and Université Paris 1)
QML Estimation of Dynamic Panel Data Models with Spatial Errors
Liangjun Su (Singapore Management University); Zhenlin Yang (Singapore Management University)
GMM-based inference in the AR(1) panel data model for parameter values where local identification fails
Edith Madsen (Technical University of Denmark)
Bias Reduction in Multilevel Dynamic Panels with Small T: An Application to the Dynamics of Corn Supply
Nathan Hendricks (Kansas State University); Aaron Smith (UC Davis)
On the use of the Arellano-Bond estimator
Christoph Hanck (University of Groningen); Laura Spierdijk (University of Groningen); Tom Wansbeek
(University of Groningen)
Session E2 - Production and Productivity 2 (Salle / Room 1)
Session Chair: Subal Kumbhakar, SUNY Binghamton (University of Stavanger)
Does China overinvest? Evidence from a panel of Chinese firms
Sai Ding (University of Glasgow); Alessandra Guariglia (Durham University); John Knight (University of Oxford)
Import Competition, Domestic Regulation and Firm-Level Productivity Growth in the OECD
Sarra Ben Yahmed (Université de la Méditerranée); Sean Dougherty (OECD)
The Evolution of Cost-Productivity and Efficiency Among U.S. Credit Unions
David Wheelock (Federal Reserve Bank of St. Louis); Paul Wilson (Clemson University)
Estimation of the Industry Production Function with Biased Technical Change: A Control Function Approach
Xi Chen (Université de Strasbourg)
Nonlinearities in productivity growth: A semi-parametric panel analysis
Theophile Azomahou (United Nations University –UNU MERIT and Maastricht University); Bity Diene
(University of Auvergne, CERDI); Mbaye Diene (University Cheikh-Anta-Diop, CRES)
Session E3 - Cross-Section Dependence and Interactions (Salle / Room 2)
Session Chair: Jean-Pierre Urbain (Maastricht University)
EC3SLS Estimator for a Simultaneous System of Spatial Autoregressive Equations with Random Effects
Badi Baltagi (Syracuse University); Ying Deng (Syracuse University)
GMM estimation of fixed effects dynamic panel data models with spatial lag and spatial errors
Pavel Cizek (Tilburg University); Jan Jacobs (University of Groningen); Jenny Ligthart (Tilburg University);
Hendrik Vrijburg (Erasmus University Rotterdam)
On bootstrapping panel factor series
Lorenzo Trapani (Cass Business School, London, UK)
Who are the Voluntary Leaders? Experimental Evidence from a Sequential Contribution Game.
Phu Nguyen-Van (Universite de Strasbourg); Raphaële Préget (INRA); Marc Willinger (Lameta)
Understanding Interactions in Social Networks and Committees: A small panel approach
Arnab Bhattacharjee (University of Dundee); Sean Holly (Cambridge University
Session E4 - Market Structure (Salle / Room 5)
Session Chair: Jean-Pierre Huiban (INRA)
Rent Building, Rent Sharing - A Panel Country-Industry Empirical Analysis
Philippe Askenazy (Paris School of Economic-CNRS); Gilbert CETTE (Banque de France); Paul Maarek (Banque
de France and Université de Cergy-Pontoise)
Market Structure, Sunk Costs and Scope Economies in Pharmaceutical R&D
Laura Magazzini (University of Verona); Fabio Pammolli (IMT Lucca Institute of Advanced Studies); Massimo
Riccaboni (IMT Lucca Institute for Advanced Studies)
The Law of One Price and the Role of Market Structure
Mustafa Caglayan (University of Sheffield); Alpay Filiztekin (Sabanci University)
Job Matching on non separated Occupational Labour Markets
Michael Stops (Institute for Employment Research)
Variation in Monopsonistic Behavior Across Establishments: Evidence From the Indonesian Labor
Peter Brummund (Cornell University)
Session E5 - R&D, Capital and Technical Change (Salle / Room 3)
Session Chair: Pierre Mohnen (Maastricht University)
Corporate investment and bank-dependent borrowers during the recent financial crisis
Andra Buca (ECB); Philip Vermeulen (ECB)
The R&D Tax Credit in France: A first assessment of the 2008 Reform
Benoit Mulkay (Université de Montpellier 1); Jacques Mairesse (ENSAE and Maastricht University)
ICT Intermediates, Growth and Productivity Spillovers
Thomas Strobel (Ifo Institute for Economic Research)
Business-Funded R&D Intensity across OECD countries: Impact and complementarity of financial support
policies to R&D
Benjamin Montmartin (Jean Monnet University of Saint-Etienne)
A General Model of Technical Change with an Application to OECD Countries
Almas Heshmati (Korea University); Subal Kumbhakar (SUNY Binghamton (University of Stavanger, Norway)
Session E6 - Specification of Panel Models (Auditorium)
Session Chair : Alain Trognon (GENES)
On the Role of Time in Nonseparable Panel Data Models
Stefan Hoderlein (Boston College); Yuya Sasaki (Brown University)
Discrete Heterogeneity Patterns in Panel Data
Stéphane Bonhomme (CEMFI); Elena Manresa (CEMFI)
The Formulation and Estimation of Random Effects Panel Data Models of Trade
Laszlo Matyas (Central European University); Cecilia Hornok (Central European University); Daria Pus (Central
European University)
Transformations for general variance covariance structures in a two-way error component model
Carlos de Porres Ortiz de Urbina (University of Geneva); Jaya Krishnakumar (University of Geneva)
Revisiting the Statistical Foundations of Panel Data Modeling
Aris Spanos (Virginia Tech)
July 6, 2012 - 16:15 - 17:15 Keynote Lecture (Auditorium)
M. Shapiro (University of Michigan)
Survey Measures of Risk Tolerance: Estimates from Panel Data
Session Chair : Denis Beau (Banque de France)
ABSTRACTS
Session A1 – Production factors
The Drivers of Rising Global Energy Demand: New Evidence
Yongfu Huang (United Nations University)
Abstract: This paper provides an exhaustive analysis of the key factors that led to rising global energy demand, based on an OECD
sample and a non-OECD sample over the period of 1980-2009. In addition to income and price elasticities transitionally examined,
this research takes into account the effects of structural change, demographic change, technological change and temperature change
on energy demand. Using newly developed panel data techniques allowing for spatial error dependence and spatial lag dependence,
this research finds evidence for the existence of spatial lag dependence, a positive but declining income elasticity, a negative price
elasticity, and the significant effects of industry/service value added, urbanization and technical innovations on energy demand.
This research have important implications for public policies to induce energy savings, develop service sector and promote energyefficient technologies towards a sustainable future.
Energy Consumption and Economic Growth: Evidence from Nonlinear Panel Cointegration and Causality Tests
Mübariz Hasanov (Hacettepe University); Tolga Omay (Cankaya University); Nuri Ucar (Hacettepe University)
Abstract: In this paper, we propose a nonlinear cointegration test for heterogeneous panels where the alternative hypothesis is an
exponential smooth transition (ESTAR) model. We apply our tests for investigating cointegration relationship between energy
consumption and economic growth for the G7 countries covering the period 1977-2007. Moreover, we estimate a nonlinear Panel
Vector Error Correction Model in order to analyze the direction of the causality between energy consumption and economic
growth. By using nonlinear causality tests we analyze the causality relationships in low economic growth and high economic
growth regimes. Furthermore, we deal with the cross section dependency problem in both nonlinear panel cointegration test and
nonlinear Panel Vector Error Correction Model.
Product and labor market imperfections and scale economies: Micro-evidence on France, Japan and the Netherlands
Sabien Dobbelaere (VU University Amsterdam); Kozo Kiyota (Yokohama National University); Jacques Mairesse (ENSAE and
Maastricht University)
Abstract: Allowing for three labor market settings, this paper relies on an extension of Hall's econometric framework for
estimating simultaneously price-cost mark-ups and scale economies. Using an unbalanced panel of 17 653 firms over the period
1986-2001 in France, 8 725 firms over the period 1994-2006 in Japan and 7 828 firms over the period 1993-2008 in the
Netherlands, we first classify 30 comparable manufacturing industries in 6 distinct regimes that differ in terms of the type of
competition prevailing in product and labor markets. For each of the three predominant regimes in each country, we then
investigate industry differences in the estimated product and labor market imperfections and scale economies. We do not only find
important regime differences across the three countries, we also observe cross-country differences in the levels of product and labor
market imperfections and scale economies within a particular regime.
Capital Utilisation and Retirement
Antoine Bonleu (Université de la Méditerranée) ; Gilbert CETTE (Banque de France) ; Guillaume Horny (Banque de France) ;
Abstract: This empirical analysis assesses the determinants of firms’ capital retirement. Particular attention is paid to the impact of
the business cycle and the capital usage intensity. Compared to previous studies, we directly control for the capital utilization and
disentangle the short-run mechanisms from the long-run ones. The analysis is carried out with an original and large firm-level
dataset.
The main results of the analysis may be summarized as follows: i) The retirement rate increases during slowdowns and decreases
during booms. This corresponds to a countercyclical capital retirement; ii) The capital retirement rate increases with the capital
usage intensity in the long run. This corresponds to a wear and tear effect, which is small compared to the countercyclical one; iii)
The capital retirement rate increases with the average age of capital; iv) The profit rate and the wage cost per capita do not have a
significant impact on the retirement rate.
Session A2 – International Economics
Real exchange rate and productivity in an OLG model
Thi Hong Thinh Doan (Université de la Méditerranée); Karine Gente (Aix-Marseille Université, DEFI)
Abstract: This article develops an overlapping generations model to show how demography and savings affect the relationship
between real exchange rate (RER) and productivity. In high-saving (low-saving) countries and/or low-population-growth-rate
countries, a rise in productivity leads to a real depreciation whereas the RER may appreciate or depreciate in high-populationgrowth-rate. Using panel data, we conclude that a rise in productivity generally causes a real exchange rate appreciation in debtor
countries and a depreciation in creditor countries, an appreciation in countries whose population growth rate is low.
Does migration foster African exports?
Hélène Ehrhart (Banque de France) ; Maëlan Le Goff (CEPII) ; Emmanuel Rocher (Banque de France) ; Raju Singh (World Bank)
Abstract: This paper assesses the impact of migration on export performances of African countries. Our contribution is twofold.
First, this is the first study to test the pro-trade effect of migration, identified in the literature, for the case of African countries.
Second, we rely on a new dataset on international bilateral migration released recently by the World Bank for the years 1980, 1990,
2000 and 2010, which was never used in the migration - trade literature.
Our empirical analysis correctly deals with the crucial issues for gravity models of both heteroscedasticity and zero bilateral flows
that were identified recently in the literature. Our results point at a positive effect of Diaspora for exports in Africa, which is larger
for African exports than the average worldwide effect. Moreover, the stimulating effect of Diasporas on exports appears to be
stronger when migrants have established within Africa than for those that settled outside Africa and is particularly beneficial for
exports of differentiated goods.
Shifting Motives: Explaining the Buildup in Official Reserves in EMEs Since the 1980s
Atish Ghosh (International Monetary Fund); Jonathan Ostry (International Monetary Fund); Charalambos Tsangarides
(International Monetary Fund)
Abstract:
Why have emerging market economies (EMEs) been stockpiling international reserves? We find
that
motives
have
varied
over
time-vulnerability
to
current
account
shocks
was
relatively
important in the 1980s but, as EMEs have become more financially integrated, factors related to the magnitude of potential capital
outflows have gained in importance. Reserve accumulation as a by-product of undervalued currencies has also become more
important since the Asian crisis. Correspondingly, using quantile regressions, we find that the reason for holding reserves varies
according to the country’s position in the global reserves distribution. High reserve holders, who tend to be more financially
integrated, are motivated by insurance against capital account rather than current account shocks, and are more sensitive to the cost
of holding reserves than are low-reserve holders. Currency undervaluation is a significant determinant across the reserves
distribution, albeit for different reasons.
Currency Crises, Exchange Rate Regimes, and Capital Account Liberalization: A Duration Analysis Approach
Mohammad Karimi (University of Ottawa); Marcel Voia (Carleton University)
Abstract: This paper empirically analyzes the effect of exchange rate regimes and capital account liberalization policies on the
occurrence of currency crises for 21 countries over the period of1970-1998. We examine changes of the likelihood of currency
crises under de jure, and de facto exchange rate regimes. We also test whether the impact of the exchange rate regimes on currency
stability would be different under free and restricted capital flows. Our findings show that the likelihood of currency crises changes
significantly under de facto regimes. However, the results are sensitive to the choice of de facto exchange rate arrangements.
Furthermore, in our sample, capital control policies appear to be helpful in preventing low duration currency crises. The results are
robust to a wide variety of sample and models checks.
Session A3 – Multidimentional Fixed Effects
Linear Regression for Panel with Unknown Number of Factors as Interactive Fixed Effects
Hyungsik Roger Moon (University of Southern California); Martin Weidner (University College London)
Abstract: In this paper we study the Gaussian quasi maximum likelihood estimator (QMLE) in a linear panel regression model
with interactive fixed effects for asymptotics where both the number of time periods and the number of cross-sectional units go to
infinity. Under appropriate assumptions we show that the limiting distribution of the QMLE for the regression coefficients is
independent of the number of interactive fixed effects used in the estimation, as long as this number does not fall below the true
number of interactive fixed effects present in the data. The important practical implication of this result is that for inference on the
regression coefficients one does not need to estimate the number of interactive effects consistently, but can simply rely on any
known upper bound of this number to calculate the QMLE.
The Estimation of Multi-dimensional Fixed Effects Panel Data Models
Laszlo Matyas (Central European University); Laszlo Balazsi (Central European University)
Abstract: The paper introduces for the most frequently used three-dimensional fixed effects panel data models the appropriate
Within estimators. It analyzes the behaviour of these estimators in the case of no-self-flow data, unbalanced data and dynamic
autoregressive models. Then the main results are generalised for higher dimensional panel data sets as well.
Session A4 – Dynamic Models 1
Semiparametrically Efficient High-Dimensional GMM Estimator with Many Invalid Moment Conditions: An Application
to Dynamic Panel Data Models
Mehmet Caner (North Carolina State University); Xu Han (North Carolina State University); Yoonseok Lee (University of
Michigan)
Abstract: This paper develops an adaptive elastic-net GMM estimator with possibly many invalid moment conditions.We allow for
the number of structural parameters
as well as the number of moment conditions increase with the sample size
The
basic idea is to conduct the standard GMM with combining two penalty terms: the quadratic regularization and the adaptively
weighted lasso shrinkage. Given many orthogonality restrictions, including the invalid ones, the new estimator uses information
only from the valid moment conditions to achieve the semiparametric efficiency bound. The estimator is thus very useful in
practice since it conduct the consistent moment selection and efficient estimation of the structural parameters simultaneously.
, which becomes slower as the number of invalid moment
However, the rate of convergence is given as
conditions
gets large. We apply the new estimation procedure to dynamic panel data models, where both the time and cross
section dimensions are large. The new estimator is robust to possible serial correlations in the regression error term, though the
standard first-difference-based GMM approach suffers from invalid moment conditions.
Nonparametric Dynamic Panel Data Models: Kernel Estimation and Specification Testing
Xun Lu (Hong Kong University of Science and Technology); Liangjun Su (Singapore Management University)
Abstract: Motivated by the first differencing method for linear panel data models, we propose a class of iterative local polynomial
estimators for nonparametric dynamic panel data models with or without exogeous regressors. The estimators utilize the additive
structure of the first-differenced model, the fact that the two additive components have the same functional form, and the unknown
function of interest is implicitly defined as a solution of a Fredholm integral equation of the second kind. We establish the uniform
consistency and asymptotic normality of the estimators. We also propose a consistent test for the correct specification of linearity
distance of our nonparametric estimates and the parametric estimates under
in typical dynamic panel data models based on the
the linear restriction. We derive the asymptotic distributions of the test statistic under the null hypothesis and a sequence of Pitman
local alternatives, and prove its consistency against global alternatives. Simulations suggest that the proposed estimators and tests
perform well in finite samples. We apply our new methods to study the relation between economic growth, initial economic
condition and capital accumulation and find the nonlinear relation between economic growth and initial economic condition.
Accuracy and efficiency of various GMM inference techniques in dynamic micro panel data models
Jan Kiviet (University of Amsterdam); Milan Pleus (University of Amsterdam); Rutger Poldermans (University of Amsterdam)
Abstract: The performance in finite samples is examined of inference obtained by variants of the Arellano-Bond and the BlundellBond 2-step GMM estimation techniques for single dynamic panel data models with cross-sectional heteroskedasticity. By
simulation the effects are examined of using less (against more) robust implementations of the GMM weighting matrix, and also of
particular instrument strength enhancing transformations of the matrix of instrumental variables. We compare the root mean
squared errors of the resulting coefficient estimators and also the size of coefficient and of different implementations of
overidentification restriction tests. Also the size and power of a test on the validity of the additional orthogonality conditions
exploited by the Blundell-Bond technique are assessed over a pretty wide grid of relevant cases. We find that particular
asymptotically optimal weighting matrices are superior to others in finite samples regarding the coefficient and (incremental)
overidentification restriction tests, although their effect on the performance in terms of bias and efficiency is negligible.
Furthermore, most of the variants of tests for overidentification restrictions show serious deficiencies.
Aggregation in Large Dynamic Panels
Hashem Pesaran (University of Cambridge and USC); Alexander Chudik (Federal Reserve Bank of Dallas and CIMF)
Abstract: This paper investigates the problem of aggregation in the case of large linear dynamic panels, where each micro unit is
potentially related to all other micro units, and where micro innovations are allowed to be cross sectionally dependent. Following
Pesaran (2003), an optimal aggregate function is derived and used (i) to establish conditions under which Granger's (1980)
conjecture regarding the long memory properties of aggregate variables from `a very large scale dynamic, econometric model'
holds, and (ii) to show which distributional features of micro parameters can be identified from the aggregate model. The paper
also derives impulse response functions for the aggregate variables, distinquishing between the effects of macro and aggregated
idiosyncratic shocks. Some of the findings of the paper are illustrated by Monte Carlo experiments. The paper also contains an
empirical application to consumer price inflation in Germany, France and Italy, and re-examines the extent to which `observed'
inflation persistence at the aggregate level is due to aggregation and/or common unobserved factors. Our findings suggest that
dynamic heterogeneity as well as persistent common factors are needed for explaining the observed persistence of the aggregate
inflation.
Session A5 – Non-Linear Models 1
Simple estimators for count data models with sample selection
Koen Jochmans (Sciences Po)
Abstract: This paper introduces simple semiparametric estimators to deal with sample selection in cross-sectional and fixed-effect
count-data models. In the cross-sectional setup, the approach is based on pairwise differencing across units. It offers a robust
alternative to the parametric estimators of Terza [Journal of Econometrics, 84 (1998), 129-154] at no cost in computational
complexity. With panel data, the estimator is constructed from weighted first-differences across time and allows to remain agnostic
about the distribution of both the idiosyncratic unobservables and the fixed effects. To the best of my knowledge, this is the first
attempt to construct a fixed-T consistent semiparametric estimator for a nonlinear fixed-effect model with sample selection.
A negative binomial model and moment conditions for count panel data
Yoshitsugu Kitazawa (Kyushu Sangyo University)
Abstract: This paper proposes some moment conditions associated with an appropriate specification of negative binomial model for
count panel data, which is proposed by Hausman et al. (1984). The newly proposed moment conditions enable researchers to
conduct the consistent estimation of the model under much weaker assumptions than those configured by Hausman et al. (1984). In
some Monte Carlo experiments, it is shown that the GMM estimators using the new moment conditions perform well in the DGP
configurations conforming to the specification above.
Nonparametric Quasi-Differencing with Applications
Kirill Evdokimov (Princeton University)
Abstract: This paper presents two sets of results on nonparametric identification. First, a nonparametric generalization of the quasidifferencing method is developed. A nonparametric panel data model is shown to be identified using three time periods of data. An
explicit characterization of the structural function is obtained. The fixed effects and idiosyncratic errors are not separable from the
covariates and hence affect the marginal effects. The structural function is allowed to vary over time in an arbitrary fashion. In
addition, a new nonparametric panel transformation model is introduced and is shown to be identified.The first result is then used
to establish nonparametric identification of several duration models with multiple spells. The existing results are substantially
extended by allowing for the nonseparability of the unobserved heterogeneity and the covariates in the specification of the hazard
rate. As an important consequence, the paper demonstrates identification of a multiple state duration model that treats unobserved
heterogeneity as a fixed effect, rather than as a random effect, as has been done in previous studies. Identification of duration
models with multivariate unobserved heterogeneity and censoring is also established.
Location choice of immigrants in Belgium 1990-2007
Hubert Jayet (Université des Sciences et Technologies de Lille) ; Glenn Rayp (Ugent) ; Ilse Ruyssen (Ghent University) ; Nadiya
Ukrayinchuk (Université Lille 2)
Abstract: This paper analyses the location choice of immigrants living in Belgium between 1990-2007 and aims at separating the so
called “network effect” from other locality-specific characteristics. The Belgian population register constitutes a rich database of
migrant inflows and stocks broken down by nationality and age cohort, which allows us to distinguish the immigrants of working
age. Using these data, we empirically explain the number of immigrants arriving in each of the 43 governmental districts as well as
the 588 municipalities. The network size is identified by the number of previous arrivals in the same location, whereas other local,
geographically-specific characteristics - such as the local labor-market conditions or the presence of higher-quality amenities - are
assumed to be time invariant covariates that generate attractiveness effects that can be measured using location specific fixed
effects. The model is estimated in two steps. First, we estimate a nested logit regression to determine the network effect as well as
the fixed effects. In a second step, the fixed effect estimates are regressed on time-invariant location characteristics in order to
define their relative importance. The results show that both local characteristics and network effects are significant factors
explaining the spatial repartition of immigrants in Belgium, where the first dominate the latter.
Session A6 – Duration and Survival Models
The Determinants of Firm Exit in the French Food Industries
Pierre Blanchard (ERUDITE, Université Paris-Est Créteil) ; Jean-Pierre Huiban (INRA and ERUDITE) ; Claude Mathieu
(ERUDITE, Université Paris-Est Créteil)
Abstract: A semi-parametric approach is used to estimate the firm propensity to exit. The unobserved individual productivity of
firm is first estimated, using the Ackerberg, Caves and Frazer (2006) approach, then introduced as determinant of firm exit next to
other variables, including the firm’s level of sunk costs and the industry concentration as expected barriers to exit. By using an
unbalanced panel of 5849 firms in French food industries from 1996 to 2002, we find a significantly negative relationship between
the probability of exit of the firm and its individual efficiency and age. Beyond those well-known results, we also show that the
amount of sunk costs may be an important barrier to exit. By the end, the relationship between the propensity to exit and the
industry level of concentration is affected by a turning point: the relationship is first an increasing one and then becomes decreasing
after.
Does Tort Law Improve the Health of Newborns, or Miscarry? A Longitudinal Analysis of the Effect of Liability Pressure
on Birth Outcomes
Michelle M. Mello (Harvard School of Public Health); David M. Studdert (University of Melbourne); S.V. Subramanian (Harvard
School of Public Health); Y. Tony Yang (George Mason University )
Abstract: Previous research has identified an association between malpractice liability risk levels in a state and greater use of
cesarean sections in obstetrical care. However, it is unclear whether such practice changes are associated with better birth
outcomes. Using a mixed-effects model, we investigate the impact of malpractice risk, as measured by malpractice insurance
premiums and various state tort reforms, on four adverse birth outcomes. We use a longitudinal research design to examine
millions of individual births from fifty-one jurisdictions over twelve years (1991-2002). We find that the odds of adverse birth
outcomes are not associated with premium levels or tort reforms. Our results suggest that rather than having a socially desirable
deterrent effect on substandard care, liability pressure may produce a level of precaution taking in obstetrics that is higher than
socially optimal. By the same token, the results also suggest that the adoption of liability-limiting reforms is unlikely to have an
adverse impact on outcomes.
The impact of health events on individual labor market histories : the message from difference in differences with exact
matching
Emmanuel Duguet (ERUDITE, Université Paris Est-Créteil); Christine LE CLAINCHE (Centre d’Études de l’Emploi)
Abstract: We studied the effect of health events (accidents and chronic diseases) on the occupation probabilities at the individual
level, while accounting for both correlated individual and time effects. Using difference-in-differences with exact matching
estimators, we found that health events have a strong impact on individual labor market histories. The workers affected by a health
event have a stronger probability of entering inactivity and a lower probability of keeping their jobs. We also found that the less
qualified workers, women and workers with short term jobs are the most negatively affected by health events.
Session B2 – Dynamic Models 2
Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models
Kazuhiko Hayakawa (Hiroshima University); Hashem Pesaran (University of Cambridge and USC)
Abstract: This paper extends the transformed maximum likelihood approach for estimation of dynamic panel data models by
Hsiao, Pesaran and Tahmiscioglu(2002) to the case where the errors are cross-sectionally heteroskedastic. This extension is not
trivial due to the incidental parameters problem that arises, and its implications for estimation and inference. We approach the
problem by working with a mis-specified homoskedastic model. It is shown that the transformed maximum likelihood estimator
continue to be consistent even in the presence of cross-sectional heteroskedasticity. We also obtain standard errors that are robust to
cross-sectional heteroskedasticity of unknown form. By means of Monte Carlo simulation, we investigate the finite sample
behavior of the transformed maximum likelihood estimator and compare it with various GMM estimators proposed in the
literature. Simulation results reveal that, in terms of median absolute errors and accuracy of inference, the transformed likelihood
estimator outperforms the GMM estimators in almost all cases.
GMM Estimation of short dynamic panel data models with cross-sectional dependence
Vasilis Sarafidis (University of Sydney)
Abstract: This paper considers estimation of short dynamic panel data models with error cross-sectional dependence. It is shown
that under spatially correlated errors, an additional, generally non-redundant, set of moment conditions becomes available for each i
- specifically, instruments with respect to the individual(s) which unit i is spatially correlated with. We demonstrate that these
moment conditions remain valid when the error term contains a common factor component, in which situation the standard moment
conditions with respect to individual i itself are invalidated, and thereby the standard dynamic panel GMM estimators are
inconsistent. The resulting estimators are computationally attractive and do not require estimating the number of unobserved
factors. Simulated experiments show that the resulting method of moments estimators perform well in terms of both median bias
and root median square error.
Pairwise difference estimation of dynamic panel data models
Michele Aquaro (Tilburg University); Pavel Cizek (Tilburg University)
Abstract: In this paper, a new estimation procedure of dynamic panel data models with fixed effects is proposed. To improve upon
existing estimators, we propose to apply the pairwise difference data transformation to the generalized method of moments based
estimators. A particular focus is given to the long difference estimation procedures of Hahn et. al. (2007) which was proved to
retain strong moment conditions even when data are persistent without imposing further assumptions. The bias and asymptotic
distribution of the resulting estimators are derived. A simulation study is conducted to assess the finite samples properties of the
estimators.
Semiparametric Estimation of Partially Varying-Coefficient Dynamic Panel Data Models
Zongwu Cai (University of North Carolina at Charlotte); Linna Chen (Xiamen University); Ying Fang (Xiamen University)
Abstract: This paper studies a new class of semiparametric dynamic panel data models, in which some coefficients are allowed to
depend on some informative variables and some regressors can be endogenous. To estimate both parametric and nonparametric
coefficients, a three-stage semiparametric estimation method is proposed. The nonparametric GMM is proposed to estimate all
coefficients firstly and the average method is used to obtain the root-N consistent estimator of parametric coefficients. At the last
stage, the estimator of varying coefficients is obtained by plugging the parametric estimator into the model. The consistency and
asymptotic normality of both estimators are derived, and furthermore, the efficient estimation of parametric coefficients is
discussed. Monte Carlo simulations verify the theoretical results and demonstrate that our estimators work well even in a finite
sample.
Dynamic Panels with MIDAS Covariates: Estimation and Fit
Lynda Khalaf (Carleton University); Maral Kichian (Bank of Canada); Charles Saunders (Carleton University); Marcel Voia
(Carleton University)
Abstract: Higher frequency data has become more readily available, but not for all series of interest many of which remain at
lower frequencies. The introduction of Mixed Data Sampling (MIDAS) time series methods has allowed researchers to efficiently
exploit information at available frequencies. MIDAS methods so far have not formally been extended to the Panel context. Because
of a dynamic context by construction, available procedures in time series do not readily extend to Panels. In this paper we introduce
MIDAS to panel regression models suitable for analysis with GMM methods of the Anderson-Hsiao/ Arrellano-Bond form.
Because MIDAS specification tests are lacking even in time series contexts, we propose inference methods that statistically embed
specification checks, as has recently been popularized in the weak-IV literature. We focus on confidence set estimation for the
MIDAS parameters for which empty outcomes signal lack of fit. Proposed confidence sets invert a model specification test which
we carefully design to adequately track the lag structure underlying GMM despite mixing time frequencies. The underlying
statistics are (asymptotically, under standard Arrellano-Bond regularity conditions) pivotal regardless of the considered data
frequency. A simulation study illustrates promising size and power properties for our procedures.
Session B3 - Production and Productivity 1
Impact of Firing Restrictions on Firm Performance: Evidence from Indonesia
Peter Brummund (Cornell University)
Abstract: This paper investigates the impact of an increase in the cost of firing employees on the performance of manufacturing
establishments in Indonesia. This analysis uses difference-in-differences methods to measure the impact of the policy change on
the output, employment, wages, and input mix of firms. The new law applied to all formal sector manufacturing establishments in
Indonesia, and increased both the size of severance payouts the firms were required to pay and the number of occasions for which
they were required to do so. Hence, in order to identify treatment and control groups, I argue that the government is unable to
enforce the law equally across all firms. I use the firms that are mostly likely to comply with the new law (large and foreign firms)
as the treatment group, and the firms that less likely to comply as the control group (small-domestic firms). I find that firms
respond to the policy and the resulting increase in total labor costs as theory would predict, decreasing output and increasing their
capital-labor ratio.
The Impact of Capital Measurement Error Correction on Firm-Level Production Function Estimation
Kamil Galuscak (Czech National Bank); Lubomir Lizal (CERGE-EI)
Abstract: Estimation of TFP hinges on the first-stage correct identification of the underlying production functions. Based on a large
panel of Czech manufacturing firms, we estimate firm-level production functions in 2003-2007 using the Levinsohn and Petrin
(2003) and Wooldridge (2009) approaches, correcting for the measurement error in capital. We show that measurement error plays
a significant role in the size of the estimated capital coefficient. The capital coefficient estimate approximately doubles (depending
on the particular industry) when we control for capital measurement error. Consequently, while the majority of industries exhibit
constant or (in)significantly decreasing returns to scale when the standard methods are used, increasing returns cannot be rejected
in some industries when the estimation is corrected for capital measurement error.
Estimation of a Panel Stochastic Frontier Model with Unobserved Common Shocks
Chih-Chiang Hsu (National Central University); Chang-Ching Lin (Academia Sinica); Shou-Yung Yin (National Central
University)
Abstract: This paper develops panel stochastic frontier models with unobserved common correlated effects. The common
correlated effects provide a way of modeling cross-sectional dependence and represent heterogeneous impacts on individuals
resulting from unobserved common shocks. Traditional panel stochastic frontier models do not distinguish between common
correlated effects and technical inefficiency. In this paper, we propose a modified maximum likelihood estimator (MLE) that does
not require estimating unobserved common correlated effects. We show that the proposed method can control the common
correlated effects and obtain consistent estimates of parameters and technical efficiency for the panel stochastic frontier model. Our
Monte Carlo simulations show that the modified MLE has satisfactory finite sample properties under a significant degree of crosssectional dependence for relatively small $T$. The proposed method is also illustrated in applications based on a cross country
comparison of the efficiency of banking industries.
Estimation and Efficiency Measurement in Stochastic Production Frontiers with Ordinal Outcomes
William Griffiths (University of Melbourne); Xiaohui Zhang (Monash University); Xueyan Zhao (Monash University)
Abstract: We consider Bayesian estimation of a stochastic production frontier with ordered categorical output, where the
inefficiency error is assumed to follow an exponential distribution, and where output, conditional on the inefficiency error, is
modelled as an ordered probit model. Gibbs sampling algorithms are provided for estimation with both cross-sectional and panel
data. New efficiency measures are suggested to overcome a lack-of-invariance problem suffered by traditional efficiency measures.
Using data from an Australian panel survey, the techniques are applied to a stochastic production frontier for individual health
production. Posterior densities are found for marginal effects, outcome probabilities, and a number of within-sample and out-ofsample efficiency measures.
A stochastic frontier model with short-run and long-run inefficiency
Roberto Colombi (Università di Bergamo); Subal Kumbhakar (SUNY Binghamton and University of Stavanger, Norway);
Gianmaria Martini (Università di Bergamo); Giorgio Vittadini (CRISP, Università di Milano-Bicocca)
Abstract: This paper presents a new stochastic frontier (SF) model for panel data. The new model moves the SF approach one step
further by taking into account unobserved firm heterogeneity, short-run and long-run inefficiency. By doing so the model can not
only separate firm heterogeneity from long-run (persistent) inefficiency, but it can also estimate both short-run and long-run
inefficiency. Previous panel data models either confounded persistent inefficiency with firm effects (heterogeneity) or firm effects
were incorrectly treated as persistent inefficiency. The model presented in this paper avoids this problem by disentangling
persistent inefficiency component from firm-effects while accommodating short-run inefficiency. Each of these components is
treated as independent random effects. We use results from closed-skew normal distribution to derive both the log-likelihood
function of the model in closed form and the posterior expected values of the random effects. These posterior expected values are
used to estimate short-run and long-run (in)efficiency as well as random firm effects. The proposed model is general enough to nest
all the currently used panel SF models and thus appropriateness of these models can be tested against the general model
empirically. We provide empirical results from three different applications using our general model as well as several popular
models that are currently used in the literature.
Session B4 - International Trade
The Hausman-Taylor Estimators on Three-level Data: An Application to Gravity Models of International Trade
Juyoung Cheong (University of Queensland); Do won Kwak (The University of Queensland); Kam Tang (School of Economics)
Abstract: This paper develops a Hausman and Taylor (1981) estimator for three-level data, such as those in the i x j x t space. The
three-level Hausman-Taylor (3LHT) estimator allows one to estimate coefficients for, in the case of bilateral trade for instance,
country-time and country-pair varying variables while controlling for unobserved country-time and country-pair heterogeneity.
This entails an extension of the class Hausman-Taylor estimator that allows one to estimate coefficient for time-invariant variable
at the presence of cross sectional fixed effects. Conditions for consistent and efficient 3LHT estimators are derived. As an
illustration of the new estimator, the paper investigates whether the standard unitary elasticity of bilateral trade flows to GDP is
robust after controlling for country-time (and country-pair) unobserved heterogeneity. As a comparison, the paper also estimates
the elasticity using the three-level Fixed Effects Instrumental Variable (3LFEIV) method that makes use of a transformation
method to remove all country-time and country-pair fixed effects.
The Elusive Impact of GATT/WTO Membership on International Trade
Laszlo Konya (La Trobe University); Laszlo Matyas (Central European University); Mark Harris (Curtin University of
Technology)
Abstract: The declared objective of the General Agreement on Tariffs and Trade (GATT) and the World Trade Organization
(WTO) is to promote free trade between member states. Nonetheless, an exhaustive study of bilateral merchandise trade based on a
large panel data set led Rose (2004) to conclude that there is no compelling empirical evidence to show that GATT/WTO
membership does actually encourage international trade. This unanticipated finding generated a great deal of attention in the
literature and several scholars put forward various explanations for it. In this paper we re-examine this issue by estimating gravity
models of international trade from a new data set which, unlike Rose’s, allows us to model exports and imports separately and to
take into consideration the extensive margin of trade. We provide a simple explanation for Rose’s puzzling negative outcome by
demonstrating that GATT/WTO membership does indeed encourage international trade once zero bilateral trade observations are
also included in the data set.
Outward Foreign Direct Investment and domestic performance: in search of a causal link
Emmanuel Dhyne (National Bank of Belgium); Selen Guerin (Vrije Universiteit Brussel)
Abstract: The aim of this paper is to examine causal effects of outward foreign direct investment on the domestic performance of
multinationals corporations (MNCs). In contrast with studies on the consequences of entry into export markets, studies on FDI are
small in number and mainly concentrate on the employment effects. Our preliminary results indicate that engaging in foreign
market via FDI does have a negative growth effect in productivity immediately after the switch. There is also some evidence that
outward FDI has a negative effect on the value added in the manufacturing sector, however this result is mainly due to Belgian
affiliates of foreign MNCs in our sample. For the services, there is no evidence in our sample that investing abroad causes a
divergence in the growth path of their efficiency (measured by TFP) and size (measured by value added and employment).
The Growth and Volatility of French Exporters
Antoine Berthou (Banque de France); Vincent Vicard (Banque de France); Jean-Charles Bricongne (Banque de France)
Abstract: Theoretical models in the industrial organization literature have shown that the age and size of firms are important
determinants of their dynamics. Our objective is to bring evidence about the respective impact of age and size on the dynamics of
firms in foreign markets. Using a census of French exports, reporting firm-destinations-product information over the period 19942008, we investigate the relationship between exporters' age and size on foreign markets and their expected growth, which can be
decomposed as the rate of survival and the net growth conditional on survival. We then decompose foreign sales' growth into the
contribution of growth on continuing markets (intensive margin), and the contribution of entry and exit on product-markets
(churning). Our results confirm that both age and size are important determinants of firms' dynamics in foreign markets, with the
age of exporters being negatively related to the net growth independently of size. We find however that the relation between the
average size and the net growth of exports is non-monotonous. Finally, the contribution of entry and exit in foreign markets to total
exports growth by firms is decreasing with both age and size.
R&D Expenditures and the Global Diversification of Export Sales
Christopher Baum (Boston College); Mustafa Caglayan (University of Sheffield); Oleksandr Talavera (Durham University)
Abstract: We empirically examine the role of diversification in export markets on firm-level R&D activities. In our investigation
we allow for heterogeneous behavior across firms and industries. To properly treat the incidence of R&D as a variable with a
sizable concentration of zeroes, we produce Tobit and Generalized Linear Model (GLM) estimates. Our results provide strong
evidence that export sales diversification across different regions induces firms to increase R&D expenditures, as they must
innovate and develop new products to maintain a competitive edge over their rivals. When we split the data into durable versus
nondurable firms, we observe that this effect is mainly operational among firms in the durable goods sector.
Session B5 - Heterogeneity, Inequalities
Economic Cost of Gender Gaps: Africa’s Missing Growth Reserve
Amarakoon Bandara Amarakoon (United Nations Development Programme)
Abstract: In this paper we apply the dynamic GMM estimator for an endogenous growth model to analyze the impact of gender
gap in effective labor â “defined as the combined effect of the gender gaps in labor force participation and education-on economic
output per worker. Our results indicate that gender gap in effective labor has a greater negative effect on the economic output per
worker in African countries than elsewhere. A one percent increase in gender gap in effective labor leads to a reduction in output
per worker by 0.43-0.49 per cent in Africa, 0.29-0.50 per cent in Sub-Saharan Africa and 0.26-0.32 per cent in the wider group of
countries from Africa and Asia. Results seem to indicate that the impact of gender gaps in effective labor could be larger in North
African countries. Our results confirm the notion that Africa is missing its full growth potential as a sizeable portion of its growth
reserve-women-is not fully utilized.
The Trend over Time of the Gender Wage Gap in Italy
Chiara Mussida (Catholic University of the Sacred Heart); Matteo Picchio (Tilburg University)
Abstract: We analyse gender wage gaps in Italy in the mid-1990s and in the mid-2000s. In this period important labour market
developments took place and they could have had a gender asymmetric impact on wages. We identify the time trends of different
components of the gender wage gap across all the wage distribution. We find that, whilst the gender wage gap became smaller at
the bottom of the wage distribution, women at the centre-top swam against the tide: the trend in female qualifications slightly
reduced the gender wage gap, but the gender relative trends in the wage structure significantly increased it.
Glass Ceilings or Glass Doors? The Role of Firms in Male-Female Wage Disparities
Mohsen Javdani (Simon Fraser University)
Abstract: I use Canadian linked employer-employee data to examine whether women face an economy-wide glass ceiling in the
labor market. The existence of an economy-wide glass ceiling would imply that females are under-represented in high wage
regions of the wage distribution, and their under-representation becomes more pronounced as we move to the top of the wage
distribution. As a consequence, the gap between male and female wages will be larger at the top of the wage distribution than at the
middle or bottom. I also measure the extent to which the economy-wide glass ceiling comes about because women are segregated
into lower-paying firms (glass doors), or because they are segregated into lower-paying jobs within firms (within-firm glass
ceilings), compared to their male counterparts. I find clear evidence that women experience an economy-wide glass ceiling that is
driven mainly by their disproportionate sorting across firm types rather than within firms. I also find that this disproportionate
sorting explains a substantial part of women’s under-representation at the top of the wage distribution. I find no evidence that
gender differences in sorting across firms can be accounted for by compensating differentials. However, my results are consistent
with predictions of an efficiency wage model where high-paying firms discriminate against females. My results also suggest that
after taking into account inter-firm gender segregation and observed worker characteristics, females still experience a sizeable
wage gap within firms that is persistent throughout the wage distribution.
Rising Inequality: transitory or permanent? New Evidence from a Panel of U.S. Tax Returns 1987-2006
Jason DeBacker (U.S. Department of the Treasury); Bradley Heim (Indiana University); Vasia Panousi (Federal Reserve Board);
Ivan Vidangos (Federal Reserve Board)
Abstract: We use a new, large, and confidential panel of tax returns from the Internal Revenue Service to shed light on the
permanent versus transitory nature of rising inequality in individual male labor earnings and in total household income, both before
and after taxes, in the United States over the period 1987-2006. Due to the quality and the significant size of our dataset, we are
able to conduct our analysis using rich and precisely estimated error-components models of income dynamics. Our main
specification finds evidence for a quadratic heterogeneous income profiles component and a random walk component in permanent
earnings, and for a moving-average component in autoregressive transitory earnings. We find that the increase in inequality over
our sample period was entirely permanent for male earnings, and predominantly permanent for household income. We also show
that the tax system, though reducing inequality, nonetheless did not materially affect its increasing trend. Furthermore, we compare
our model-based findings against those of simpler, non-model based inequality decomposition methods. We show that the results
for the trends in the evolution of the permanent and transitory variances are remarkably similar across methods, whereas the results
for the shares of those variances in cross-sectional inequality differ widely. Further investigation into the sources of these
differences suggests that simpler methods produce erroneous decompositions because they cannot flexibly capture the relative
degree of persistence of the transitory component of income.
To what extent are US regional incomes converging?
Mark Holmes (Waikato University); Jesus Otero (Universidad del Rosario); Theodore Panagiotidis (University of Macedonia)
Abstract: Long-run income convergence is investigated in the context of US regional data. We employ a novel pair-wise
econometric procedure based on a probabilistic definition of convergence. The idea behind this is that the time-series properties of
all the possible regional income pairs are examined by means of unit root and non-cointegration tests where inference is based on
the fraction of rejections. We distinguish between the cases of strong convergence, where the implied cointegrating vector is [1,-1],
and weak convergence, where long-run homogeneity is relaxed. In order to address cross-sectional dependence, we employ a
bootstrap methodology to derive the empirical distribution of the fraction of rejections. Overall, the evidence in favour of
convergence at state-level is weak insofar as it is only based on cointegration without homogeneity. We find that the strength of
convergence between states decreases with distance and initial income disparity. Using MSA level data, the evidence for
convergence is stronger.
Session B6 - Banking
Collateralization, Bank Loan Rates and Monitoring: Evidence from a Natural Experiment
Geraldo Cerqueiro (Universidade Católica Portuguesa); Steven Ongena (Tilburg University); Kasper Roszbach (Sveriges Riksbank
and University of Groningen)
Abstract: We study a change in the Swedish law that exogenously reduced the value of all outstanding company mortgages, i.e., a
type of collateral that is comparable to the floating lien. We explore this natural experiment to identify how collateral determines
borrower quality, loan terms, access to credit and bank monitoring of business term loans. Using a differences-in-differences
approach, we find that following the change in the law and the loss in collateral value borrowers pay a higher interest rate on their
loans, receive a worse quality assessment by their bank, and experience a substantial reduction in the supply of credit by their bank.
The reduction in collateral value also precedes a decrease in bank monitoring intensity and frequency of both the collateral and the
borrower, consistent with models in which the pledging of risky assets incentivizes banks to monitor.
Cost of funds, credit risk and bank loan interest rates in the crisis. What do micro data tell us?
Sanvi Avouyi-Dovi (Banque de France); Guillaume Horny (Banque de France); Patrick Sevestre (Banque de France and Paris
School of Economics)
Abstract: The aim of this paper is twofold. First, we provide a thorough description of the evolution of debtor interest rates for
several categories of bank loans to non-financial corporations before and during the recent crisis. Second, we assess the changes in
banks' valuation of risk induced by the crisis. We show in particular that the strong decline in the ECB refinancing rate that
followed the collapse of Lehman Brothers allowed banks to increase the spread between interest rates they charged to firms and the
ECB rate. Then, we show that this increase in the spread did not affect all types of loans, nor all firms, equally. In particular, low
risk, large and well-established firms almost fully benefited from this decrease while smaller and younger firms did not and may
even have seen the cost of their loans slightly increase. This discrepancy seems to have remained at least until the fourth quarter of
2010, the last period of observation currently available.
Credit Ratings and Bank Monitoring Ability
Leonard Nakamura (Federal Reserve Bank of Philadelphia); Kasper Roszbach (Sveriges Riksbank and University of Groningen)
Abstract: In this paper we use credit rating data from two large Swedish banks to elicit evidence on banks’ loan monitoring ability.
For these banks, our tests reveal that the banks’ credit ratings indeed include valuable private information from monitoring, as
theory suggests. However, our tests also reveal that publicly available information from a credit bureau is not efficiently
impounded in the bank ratings: The credit bureau ratings predict future movements in the bank ratings and also improve forecasts
of bankruptcy and loan default. We investigate explanations for these findings and show that they are not due to the staggered
timing of rating information updating and are unlikely to be due to the discrete nature of the ratings. We tentatively conclude that it
has proved difficult for these banks to aggregate soft and hard information. The methods we use represent a new basket of
straightforward techniques that enable both financial institutions and regulators to assess the performance of credit rating systems.
In our particular case, risk analyses by the banks should be improved; in the meantime, risk analysis of the banks’ portfolios should
be based on both internal bank ratings and public credit bureau ratings.
Rescue packages and bank lending
Michael Brei (EconomiX, Université Paris Ouest); Leonardo Gambacorta (Bank for International Settlements); Goetz von Peter
(Bank for International Settlements)
Abstract: This paper examines whether the rescue measures adopted during the global financial crisis helped to sustain the supply
of bank lending. The analysis proposes a setup that allows testing for structural shifts in the bank lending equation, and employs a
novel dataset covering large international banks headquartered in 14 major advanced economies for the period 1995â “2010.
While stronger capitalisation sustains loan growth in normal times, banks during a crisis can turn additional capital into greater
lending only once their capitalisation exceeds a critical threshold. This suggests that recapitalisations may not translate into greater
credit supply until bank balance sheets are sufficiently strengthened.
Bank leverage shocks and the macroeconomy: a new look in a data-rich environment
Jean-Stéphane Mesonnier (Banque de France); Dalibor Stevanovic (Université du Québec à Montréal)
Abstract: The recent crisis has revealed the potentially dramatic consequences of allowing the build-up of an overstretched
leverage of the financial system and prompted proposals by bank upervisors to significantly tighten bank capital requirements as
part of the new Basel 3 regulations. Although these proposals have been fiercely debated ever since, the empirical question of the
macroeconomic consequences of new regulations tightening bank capital requirements remains still largely unsettled. In this paper,
we aim at overcoming some longstanding identification issues hampering such assessments and propose a new approach based on a
data-rich environment at both the micro (bank) level and the macro level, using a combination of bank panel regressions and
macroeconomic factor models. We first construct a series aggregating innovations to the capital-to-assets ratio of a selection of
large US banks, while controlling for the possible influence of other macroeconomic shocks. We then compute impulse responses
of a large array of real, financial and credit indicators to our bank leverage shock, using the new and flexible factor methodology
developped by Ng and Stevanovic (2012). We find significant and robust evidence of a contractionary impact of an unexpected
shock reducing the leverage of large banks.
Session C1 - Econometric Theory 1
A Heteroskedasticity Robust Breusch-Pagan Test for Contemporaneous Correlation in Dynamic Panel Data Models
Andreea Halunga (University of Exeter); Chris Orme (University of Manchester); Takashi Yamagata (University of York)
Abstract: This paper proposes a heteroskedasticity-robust Breusch-Pagan test of the null hypothesis of zero cross-section (or
contemporaneous) correlation in linear panel data models. The procedure allows for either fixed, strictly exogenous and/or lagged
dependent regressor variables, as well as quite general forms of both non-normality and heteroskedasticity in the error distribution.
Whilst the asymptotic validity of the test procedure, under the null, is predicated on the number of time series observations, T,
being large relative to the number of cross-section units, N, independence of the cross-sections is not assumed. Across a variety of
experimental designs, a Monte Carlo study suggests that, in general (but not always), the predictions from asymptotic theory
provide a good guide to the finite sample behaviour of the test. In particular, with skewed errors and/or when N/T is not small,
discrepancies can occur. However, for all the experimental designs, any one of three asymptotically valid wild bootstrap
approximations (that are considered in this paper) gives very close agreement between the nominal and empirical significance
levels of the test. Moreover, in comparison with wild bootstrap "version" of the original Breusch-Pagan test (Godfrey and
Yamagata, 2011) the corresponding version of the heteroskedasticity-robust Breusch-Pagan test is more reliable. As an illustration,
the proposed tests are applied to a dynamic growth model for a panel of 20 OECD countries.
A Consistent Nonparametric Test of Parametric Regression Functional Form in Fixed Effects Panel Data Models
Qi Li (Texas A&M University); Yiguo Sun (University of Guelph)
Abstract: We propose a consistent nonparametric test to test for a parametric linear functional form against a nonparametric
alternative in the framework of fixed effects panel data models. The proposed test statistic is based on an integrated squared
difference between a parametric and a non-iterative kernel curve estimate. We show that the test has a limiting standard normal
distribution under the null hypothesis of a linear fixed effects panel data model, and show that the test is a consistent test. We also
establish the asymptotic validity of a bootstrap procedure which is used to better approximate the finite sample null distribution of
the test statistic. Simulation results show that the proposed test performs well for panel data with a large number of cross-sectional
units and a finite number of observations across time.
LM-type tests for slope homogeneity in panel data models
Christoph Roling (Bonn Graduate School of Economics); Joerg Breitung (University of Bonn); Nazarii Salish (University of Bonn)
Abstract: We propose a Lagrange Multiplier (LM) test for homogeneous slopes in non-dynamic panel data models. We consider
two variations of the standard LM test. The first is an asymptotically equivalent regression-based test and the second incorporates
the one-sided nature of the alternative hypothesis. We examine the small-sample properties of the LM-type tests relative to Pesaran
and Yamagata's (2008) dispersion statistic by simulation and find that the LM tests perform well in small samples, in particular
when the cross-section dimension is large relative to the time dimension.
Analyzing Treatment Effects on Distributions with Complex Structure
Marcel Voia (Carleton University); Mark Bebbington (Massey University); Christopher Bennett (Vanderbilt University); Ricardas
Zitikis (The University of Western Ontario)
Abstract: Comparing treatment effects using, for example, the average treatment or local average treatment effect is not
particularly informative about specific regions of the treatment and control distributions and their subpopulations. For this reason,
in the present paper we consider two type of tests: 1. tests which are based on entire distributions and thus provide a more
informative analysis of treatments and their effectiveness, especially when the distribution of the outcome variable of interest is
complicated in nature (here we consider a duration outcome variable). In particular the tests proposed in this paper attempt to deal
with discontinuities in the distributions of interests by fitting a mixture of duration models. We illustrate the performance of the
tests on subpopulations of the most effective treatment from the Pennsylvania Bonus Experiment (PBE), and then use a simulation
study based on mixture models fitted to the PBE subpopulations data to examine the power of the suggested tests. 2. tests that
examine the timing of exit for any evidence of strategic behaviour. This additional test is used to understand differences in
observed outcomes in su-populations due to the treatment incentive.
Session C2 - Human Capital, Employment and Wages
Do government purchases affect unemployment?
Steinar Holden (University of Oslo); Victoria Sparrman (Statistics Norway)
Abstract: We investigate empirically the effect of government purchases on unemployment in 20 OECD countries, for the period
1960-2007. Compared to earlier studies we use a data set with more variation in unemployment, and which allows for controlling
for a host of factors that influence the effect of government purchases. We find that increased government purchases lead to lower
unemployment; an increase equal to one percent of GDP reduces unemployment by 0.2 percentage point in the same year. The
effect is greater in downturns than in booms, and also greater under a fixed exchange rate regime than under a floating regime.
The Design of Unemployment Transfers: Evidence from a Dynamic Structural Life-cycle Model
Peter Haan (Deutsches Institut für Wirtschaftsforschung, DIW); Victoria Prowse (University of Oxford)
Abstract: In this paper we use a dynamic structural life-cycle model to analyze the employment, fiscal and welfare effects induced
by unemployment insurance. The model features a detailed specification of the tax and transfer system, including unemployment
insurance benefits which depend on an individual's employment and earnings history. The model also captures the endogenous
accumulation of experience which impacts on future wages, job arrivals and job separations. For better identification of the
structural parameters we exploit a quasi-natural experiment, namely reductions over time in the entitlement period for
unemployment insurance benefits which varied by age and experience. The results show that a policy cut in the generosity of
unemployment insurance operationalized as a reduction in the entitlement period generates a larger increase in employment and
yields a bigger fiscal saving than a cut operationalized as a reduction in the replacement ratio. Welfare analysis of revenue neutral
tax and transfer reforms also favors a reduction in the entitlement period.
Youth labour market histories, neighborhood of origin and diploma: a dynamic modeling
Thierry Kamionka (CNRS); Xavier Vu Ngoc (Ecole Polytechnique)
Abstract: We use the survey Generation 98 to study the trajectories of youth between five states of the labour market: permanent
employment contracts, temporary employment contracts, subsidized employments, unemployment, non-participation. These youths
are leaving education system in 1998. We use a dynamic multinomial logit model with random effects in order to study labour
market transitions of young workers. Initial conditions are treated using the method proposed by Heckman (1981). Initial
conditions consist in the neighborhood of origin at the time they leave the education system, the level of the diploma and the initial
position on the market. Censoring on the location at the time they leave education is formally taken into account in modeling. The
model we use allow to disentangle true from spurious state dependence. We assess the impact of neighborhood of origin on youth
labour market histories. We evaluate the impact of subsidized employment on labour market transitions on a recent period.
Financial incentives and study duration in Higher Education
Trude Gunnes (Statistics Norway); Lars J. Kirkebøen (Statistics Norway); Marte Rønning (Statistics Norway)
Abstract: The current paper investigates to which extent students in higher education respond to financial incentives by adjusting
their study behavior. Students in Norway who completed certain graduate study programs between 1991 and 1995 on stipulated
time were entitled to a restitution (of approximately 3,000 USD) from the Norwegian State Educational Loan
Fund. Using a difference-in-difference approach, we find that the fraction of students graduating on time during the reform period
increased by 10 percent, relative to a base probability of about 25 percent. The estimated effect for fully treated students (students
who were aware of the reform from the start of their studies) is much higher, at 50 percent.
Human capital investments and the life cycle variance of earnings
Thierry Magnac (University Toulouse 1 Capitole); Nicolas Pistolesi (Toulouse School of Economics (GREMAQ)); Sébastien Roux
(CREST/DARES)
Abstract: We propose a model of on-the-job human capital investments in which individuals differ in their initial human capital,
their rate of return, their costs of human capital investments and their terminal values of human capital at retirement. We derive a
tractable reduced form Mincerian model of log wage profiles along the life cycle which is written as a function of three individual
specific factors. The model is estimated by pseudo maximum likelihood using panel data for a single cohort of French wage earners
observed over a long span of 30 years. This structure allows us to compute counterfactual profiles in which returns and terminal
values are modified and we show how wage inequality is affected by these changes over the life-cycle.
Session C3 - Household Finance
The Free Installment Puzzle
Sungjin Cho (Seoul National University); John Rust (University of Maryland)
instruments; matching estimators; discrete choice model; censoring; identification
Abstract: We analyze a new dataset on borrowing decisions of a sample of customers of a credit card company. This credit card
allows customers to pay for their purchases via it installment credit over terms up to 12 months at an interest rate that depends on
the customer's credit score and the duration of the installment loan. We use these data to estimate the effect of interest rates on
consumers' demand for credit. We show that conventional econometric methods (including regression, instrumental variables, and
matching estimators) predict that the demand for installment credit is an increasing function of the interest rate, an inference we
dismiss as spurious due to the endogeneity of the interest rate and the effect of unobserved credit constraints that cause customers
with worse credit scores to have higher demand for installment credit. To make more credible inferences about the effect of interest
rates on the demand for credit we exploit a novel feature in our data: customers are more or less randomly offered free installments,
i.e. the opportunity to pay back a given purchase over a fixed term ranging from 2 to 12 months at an interest rate of zero. We
exploit these free installment offers as a quasi-random experiment the help identify the demand for credit by estimating a discrete
choice model of the installment credit decision that accounts for censoring (choice based sampling) in observed free installments.
Despite the significant censoring, we show that it is possible to identify consumers' choice probabilities and the probability they are
offered free installments. The free installment puzzle results from our finding that less than 3\% of the transactions in our sample
were made as free installments, even though our model predicts that the average probability of being offered a free installment in
our sample is approximately 20\%. Our model predicts a high incidence of ``pre-commitment behavior'' even among the minority
of individuals who do take the free installment offers. For example, the model predicts that 88\% of individuals who were offered
(and chose) a 10 month free installment offer pre-commited at time of purchase to pay the balance in fewer than 10 installments.
This pre-commitment behavior is puzzling since there are no pre-payment penalties, and traditional economic models predict that
consumers should choose the maximum loan duration when a loan is offered at a 0 % interest rate. This puzzling consumer
behavior raises questions about the company's behavior: why does it make so many free installment offers if the response to them is
so poor? We also present evidence that the increasing interest rate schedule the company offers its customers may not be profitmaximizing.
An alternative mode of dealing with personal bankruptcy: the French household over-indebtedness commissions’
experience
Henri Fraisse (Banque de France); Philippe Frouté (Université de Paris-Est Créteil)
Abstract: The economic crisis has engendered a worldwide increase in the number of personal bankruptcies. Some countries have
implemented or are willing to implement programs helping households to restructure their debt. This paper evaluates ex-post the
role of the conciliation boards in charge of the French dispute resolution system, with respect to the creditors’ recovery rate and the
household’s redefault rate. The random allocation of the households over managers of different pro-household friendliness is used
as an instrumental variable to identify the causal effects of the system on case outcomes. Sixty percent of households are ordered to
repay part of their debt. Over a two year horizon, they redefault at an eleven percent rate and reimburse twenty two percent of their
initial outstanding debt. Our results highlight a substantial impact of the severity of the case manager: the probability of being
required to pay something increases by ten percentage points when being assigned with a tough manager. It also diminishes the
average fraction of reimbursed debt but increases the total collected amounts. The impact on the redefault rate is moderate.
A Panel Latent Class Tobit Model:An application to Modelling Charitable Donations
Sarah Brown (University of Sheffield); William H. Greene (New York University); Mark Harris (Curtin University of
Technology); Karl Taylor (University of Sheffield)
Abstract: We make a methodological contribution to the latent class literature by re-examining censored variable analysis within a
panel data context. Specifically, we extend the standard latent class tobit panel approach to include random effects, to allow for
heteroskedasticity and to incorporate the inverse hyperbolic sine (IHS) transformation of the dependent variable. The IHS
transformation ensures robustness to non-normality in the original (untransformed) dependent variable. We then use this
framework to model charitable donations, which is an interesting application given the potential for divergent groups of individuals
in the population with regard to their donating behaviour, which is exactly what will be uncovered by a latent class approach. Our
findings, which are based on U.S. panel data drawn from five waves of the Panel Study of Income Dynamics, do indeed find two
distinct classes. We find a clear disparity between the probabilities of zero donations across these classes, with one class,
dominated by the observed zero givers, being associated with relatively low levels of predicted giving. We find clear evidence of
both heteroskedasticity and random effects. All IHS parameters were significantly different from zero and different across classes.
In combination, these findings endorse the importance of our three modelling extensions.
Session C4 - R&D, Innovation and Productivity
Innovation and Welfare: Results from joint estimation of production and demand functions
Jordi Jaumandreu (Boston University); Jacques Mairesse (CREST-ENSAE and Maastricht University)
Abstract: This paper develops a simple framework to estimate the parameters of the production function together with the elasticity
of the demand for the output and the impact of demand and cost shifters. The use of this framework helps, in the first place, to treat
successfully the difficult problem of the endogeneity of input quantities. But it also provides a natural way to assess the welfare
effects of firms' innovative actions by estimating their impact on both cost and demand. We show that the total current period
(static) welfare gains of introducing a process or a product innovation are, on average, about 1.6% and 4%, respectively, of the
value of the firm's current sales. The increase in consumer surplus amounts to two- thirds of these gains in the first case and half in
the second.
Productivity in China's High Technology Industry: Regional Heterogeneity and R&D
Rui Zhang (Sichuan University); Kai Sun (Aston University); Michael S. Delgado (Binghamton University); Subal C. Kumbhakar
(Binghamton University)
Abstract: This paper analyzes the impact of Research and Development (R&D) on the productivity of China's high technology
industry. In order to capture important differences in the effect of R&D on output that arise from geographic and socioeconomic
differences across three major regions in China, we use a novel semiparametric approach that allows us to model heterogeneities
across provinces and time. Using a unique provincial level panel dataset spanning the period 2000-2007, we find that the impact of
R&D on output varies substantially in terms of magnitude and significance across different regions. Results show that the eastern
region benefits the most from R&D investments, however it benefits the least from technical progress, while the western region
benefits the least from R&D investments, but enjoys the highest benefits from technical progress. The central region benefits from
R&D investments more than the western region and benefits from technical progress more than the eastern region. Our results
suggest that R&D investments would significantly increase output in both the eastern and central regions, however technical
progress in the central region may further compound the effects of R&D on output within the region.
Spillovers and Strategic Dynamics in Product Innovation: Empirical Evidence from Japanese National Innovation Survey
2009 and Implications for Public Financial Support
Daiya Isogawa (University of Tokyo); Hiroshi Ohashi (University of Tokyo)
Abstract: This paper estimates a dynamic oligopoly model of product innovation to evaluate an equilibrium effect of public policy
on a firm's innovation activity. The model allows for a firm's dynamic decision on entry and exit and on innovation activity. The
paper considers a multi-agent Markov-Perfect Nash Equilibrium of the dynamic oligopoly game, and estimates its primitives by a
method from Bajari, Benkard, and Levin (2007, Econometrica). The estimation uses unique panel data mainly coming from
Japanese National Innovation Survey 2009. With estimated parameters, the paper conducts counterfactual exercises to assess the
effect of public financial support for a firm's innovation activity. Estimates reveal that positive spillovers and dynamic interdependence exist in firms' innovation activity. The counterfactual exercises also indicate that the public financial support
encourages a firm's innovation activity and improve its performance, but most of the support goes to a firm that would conduct
innovation activities even without it.
The Size Does Matter: New Evidence on the Effect of Intellectual Property Rights on Innovation
Alexandru Minea (Université d’Auvergne)
Abstract: I investigate the effect of intellectual property rights (IPR) on innovation using modern econometric techniques, which
allow dealing with endogenous thresholds on panel data. Estimations performed on a sample of both developing and developed
countries emphasize the presence of important nonlinearities in the impact of IPR on innovation. Thus, an IPR tightening can
increase or decrease innovation, depending on the size of the IPR level. Moreover, I split the sample and analyze the link between
IPR and innovation in developing, respectively developed countries. Contrary to previous studies, I find that the IPR level plays a
significant role, regarding both the sign and the magnitude of the effect of IPR on innovation. In particular, I show the existence of
an inverted-U curve between IPR and innovation in developing countries, and present proofs supporting the presence of a second
inverted-U curve, for developed countries. These results corroborate the theoretical conclusions of the optimal patent literature,
emphasizing the existence of optimal innovation-maximizing IPR levels.
How important is innovation? A Bayesian factor-augmented productivity model on panel data
Georges Bresson (Université Paris II / Sorbonne Université); Jean-Michel Etienne (Universite Paris-Sud 11); Pierre Mohnen
(Maastricht University)
Abstract: This paper proposes a Bayesian approach to estimate a factor augmented productivity equation. We exploit the panel
dimension of our data and distinguish individual-specific and time-specific factors. On the basis of 21 technology, infrastructure
and institution indicators from 89 countries over a 19-year period (1990 to 2008), we construct summary indicators of these three
components and estimate their effect on the growth and the international differences in GDP per capita.
Session C5 - Cross-section Dependence and Interactions
A Nonlinear Panel Unit Root Test under Cross Section Dependence
Mario Cerrato (University of Glasgow); Christian de Peretti (University of Lyon 1); Rolf Larsson (Uppsala University); Nicholas
Sarantis (London Metropolitan University)
Abstract: We propose a nonlinear heterogeneous panel unit root test for testing the null hypothesis of unit-roots processes against
the alternative that allows a proportion of units to be generated by globally stationary ESTAR processes and a remaining non-zero
proportion to be generated by unit root processes. The proposed test is simple to implement and accommodates cross sectional
dependence. We show that the distribution of the test statistic is free of nuisance parameters as (N, T) −>inf. Monte Carlo
simulation shows that our test holds correct size and under the hypothesis that data are generated by globally stationary ESTAR
processes has a better power than the recent test proposed in Pesaran [2007]. Various applications are provided.
Exponent of Cross-sectional Dependence: Estimation and Inference
Natalia Bailey (University of Cambridge); George Kapetanios (Queen Mary, University of London); Hashem Pesaran (University
of Cambridge and USC)
Abstract: An important issue in the analysis of cross-sectional dependence which has received renewed interest in the past few
years is the need for a better understanding of the extent and nature of such cross dependencies. In this paper we focus on measures
of cross-sectional dependence and how such measures are related to the behaviour of the aggregates defined as cross-sectional
averages. We endeavour to determine the rate at which the cross-sectional weighted average of a set of variables appropriately
demeaned,
tends
to
zero.
One
parameterisation
sets
this
to
be
, for 1/2 <
1. Given the fashion in which it arises, we refer to
as the exponent of cross-sectional dependence.
We derive an estimator of
from the estimated variance of the cross-sectional average of the variables under consideration. We
propose bias corrected estimators, derive their asymptotic properties and consider a number of extensions. We include a detailed
Monte Carlo study supporting the theoretical results. Finally, we undertake an empirical investigation of using the S&P 500
data-set, and a large number of macroeconomic variables across and within countries.
A Nonlinear Panel Data Model of Cross-Sectional Dependence
George Kapetanios (Queen Mary, University of London); James Mitchell (Niesr); Yongcheol Shin (University of York)
Abstract: This paper proposes a nonlinear panel data model which can generate endogenously both `weak' and `strong' crosssectional dependence. The model's distinguishing characteristic is that a given agent's behaviour is influenced by an aggregation of
the views or actions of those around them. The model allows for considerable flexibility in terms of the genesis of this herding or
clustering type behaviour. At an econometric level, the model is shown to nest various extant dynamic panel data models. These
include panel AR models, spatial models, which accommodate weak dependence only, and panel models where cross-sectional
averages or factors exogenously generate strong, but not weak, cross sectional dependence. An important implication is that the
appropriate model for the aggregate series becomes intrinsically nonlinear, due to the clustering behaviour, and thus requires the
disaggregates to be simultaneously considered with the aggregate. We provide the associated asymptotic theory for estimation and
inference. This is supplemented with Monte Carlo studies and two empirical applications which indicate the utility of our proposed
model as both a structural and reduced form vehicle to model different types of cross-sectional dependence, including evolving
clusters.
Cross Section Dependence in Panel Data Efficiency Models for Technological Convergence
Camilla Mastromarco (University of Salento); Laura Serlenga (University of Bari); Yongcheol Shin
(University of York)
Abstract: In this paper we propose different approaches that deal with cross section dependence in stochastic frontier models, we
consider both weak and strong cross section dependence introducing exogenous and endogenous factors. Using stochastic frontier
panel model we investigate the technical efficiency of 26 OECD countries during 1970-2009. Our approach highlights the
importance of cross section dependence in macro productivity analysis. The potential of this type of cross sectional data
dependency is particularly important when the purpose of research is to analyze the productivity differences across countries.
Testing for Cross-Sectional Dependence in a Panel Factor Model Using the Wild Bootstrap F-Test
Badi Baltagi (Syracuse University); Chihwa Kao (Syracuse University); Sanggon Na (Syracuse University)
Abstract: This paper considers testing for cross-sectional dependence in a panel factor model. Based on the model considered by
Bai (2003), we investigate the use of a simple F-test for testing for cross-sectional dependence when the factor may be known or
unknown. The limiting distributions of these F-test statistics are derived when the cross-sectional dimension and the time-series
dimension are both large. The main contribution of this paper is to propose a wild bootstrap F-test which is shown to be consistent
and which performs well in Monte Carlo simulations especially when the factor is unknown.
Session C6 - Quantile Regression
Is there income convergence between Latin America and East Asia? An investigation using quantile regression
Geovana Bertussi (Universidade de Brasília - UnB); Lízia de Figueiredo (UFMG)
Abstract: In this paper, we evaluate the income convergence hypothesis in Latin America and East Asia between 1960 and 2000
through the use of quantile regressions to estimate growth equations. This approach allows us to assess how the effect of policy
variables on per worker income growth rate can vary over the conditional growth distribution. The results show that the income
convergence process is a local phenomenon, and not a global experience along the conditional growth distribution, that is, each
quantile exhibits an income growth behavior that is different from the rest.
Semiparametric Quantile Panel Data Models with An Application to Estimating the Growth Effect of FDI
Zongwu Cai (University of North Carolina at Charlotte); Linna Chen (Xiamen University); Ying Fang (Xiamen University)
Abstract: In this paper, we propose a semiparametric quantile panel data model with correlated random effects in which some
coefficients are allowed to depend on some smooth economic variables while other coefficients remain constant. A three-stage
estimation procedure is proposed and then their asymptotic normality is established as well. We show that the estimator of constant
coefficients is root-N consistency but the estimator of varying coefficients converges in a nonparametric rate. A small scale Monte
Carlo simulation is conducted to examine the finite sample performance. Finally, we apply the noval semiparametric quantile panel
data model to estimate the impact of FDI on economic growth using cross-country data from 1970 to 1999.
Nonparametric Identification in Panels using Quantiles
Ivan Fernandez-Val (Boston University); Stefan Hoderlein; Hajo Holzmann; Whitney Newey (MIT)
Abstract: A fundamental idea for using panel data to identify the ceteris paribus effect of x on y is to use changes in x over time to
estimate the effect. In order for changes over time in x to correspond to ceteris paribus effects, the distribution of variables other
than x must not vary over time. This restriction is like &quot;time being randomly assigned.&quot; In this paper we consider
identification via such time homogeneity conditions. Here we consider the identifying power of time homogeneity for general
nonseparable models with continuous regressors. We allow for multidimensional heterogeneity, as motivated by the economic and
empirical examples of Browning and Carro (2007). Because time homogeneity often seems to be rejected in economic applications,
we also allow for some time effects. Hoderlein and White (2009) previously derived conditional mean effects with continuous
regressors. We extend those results to quantiles, finding that with only time homogeneity and some smoothness conditions we can
identify the expected derivatives for certain subpopulations. These quantile effects are panel analogs of the effects identified by
Hoderlein and Mammen (2009). We also give results on a second kind of quantile effect, that involves conditioning on a linear
combination of dependent variables in two periods.
Quantile Regression Estimation of Panel Duration Models with Censored Data
Matthew Harding (Stanford University); Carlos Lamarche (University of Oklahoma)
Abstract: This paper studies the estimation of quantile regression panel duration models. We allow for the possibility of
endogenous covariates and correlated individual effects in the quantile regression models. We propose a quantile regression
approach for panel duration models under conditionally independent censoring. The procedure involves minimizing â„“1 convex
objective functions and is motivated by a martingale property associated with survival data in models with endogenous covariates.
We carry out a series of Monte Carlo simulations to investigate the small sample performance of the proposed approach in
comparison with other existing methods. An empirical application of the method to the analysis of the effect of unemployment
insurance on unemployment duration illustrates the approach.
Average and Quantile Effects in Nonseparable Panel Models
Victor Chernozhukov (MIT); Ivan Fernandez-Val (Boston University); Jinyong Hahn (UCLA); Whitney Newey (MIT)
Abstract: Nonseparable panel models are important in a variety of economic settings, including discrete choice. This paper gives
identification and estimation results for nonseparable models under time homogeneity conditions that are like “time is randomly
assigned” or “time is an instrument”. Partial identification results for average and quantile effects are given for discrete regressors,
under static or dynamic conditions, in fully nonparametric and in semiparametric models, with time effects. It is shown that the
usual, linear, fixed-effects estimator is not a consistent estimator of the identified average effect, and a consistent estimator is
given. A simple estimator of identified quantile treatment effects is given, providing a solution to the important problem of
estimating quantile treatment effects from panel data. Bounds for overall effects in static and dynamic models are given. The
dynamic bounds provide a partial identification solution to the important problem of estimating the effect of state dependence in
the presence of unobserved heterogeneity. The impact of T, the number of time periods, is shown by deriving shrinkage rates for
the identified set as T grows. We also consider semiparametric, discrete-choice models and find that semiparametric panel bounds
can be much tighter than nonparametric bounds. Computationally-convenient methods for semiparametric models are presented.
We propose a novel inference method that applies in panel data and other settings and show that it produces uniformly valid
confidence regions in large samples. We give empirical illustrations.
Session D1 - Factor Models
Evaluating factor pricing models using high frequency panels
Yoosoon Chang (Indiana University); Hwagyun Kim (Texas A&M University); Joon Park (Indiana University)
Abstract: This paper develops a new framework and statistical tools to analyze stock returns using high frequency data. We
consider a continuous-time multi-factor model via a continuous-time multivariate regression model incorporating realistic
empirical features, such as persistent stochastic volatilities with leverage effects. We find that conventional regression approach
often leads to misleading and inconsistent test results. We overcome this by using samples collected at random intervals, which are
set by the clock running inversely proportional to the market volatility. We find that the size factor has difficulty in explaining the
size-based portfolios, while the book-to-market factor is a valid pricing factor.
A Stochastic Discount Factor Approach to Asset pricing using panel data asymptotics
Fabio Araújo (Princeton University); Joao Victor Issler (Getulio Vargas Foundation)
Abstract: Using the Pricing Equation in a panel-data framework, we construct a novel consistent estimator of the stochastic
discount factor (SDF) which relies on the fact that its logarithm is the “common feature” in every asset return of the economy. Our
estimator is a simple function of asset returns and does not depend on any parametric function representing preferences. The
techniques discussed in this paper were applied to two relevant issues in macroeconomics and finance: the first asks what type of
parametric
preferencerepresentation could be validated by asset-return data, and the second asks whether or not our SDF estimator can price returns in an
out-of-sample forecasting exercise. In formal testing, we cannot reject standard preference specifications used in the macro/finance
literature. Estimates of the relative risk-aversion coefficient are between 1 and 2, and statistically equal to unity. We also show that
our SDF proxy can price reasonably well the returns of stocks with a higher capitalization level, whereas it shows some diffi- culty
in pricing stocks with a lower level of capitalization.
Efficient Estimation of Nonstationary Factor Models
In Choi (Sogang University)
Abstract: This paper studies the generalized principal component estimator (GPCE) of Choi (2007) for the factor model
where
is a unit-root process. First, this paper derives asymptotic distributions of the GPCEs of the factor
and factor-loading spaces which show that the GPCE enjoys an efficiency gain over the conventional principal component
estimator. Second, this paper extends the conventional static factor model to those with time polynomials, and studies the GPCE
for the models. The GPCE continues to have an efficiency gain over the conventional principal component estimator for the
extended model. Third, this paper considers the forecasting regression that uses the GPCE-based estimates of nonstationary factors
and shows that the GPCE yields more accurate forecasts than the conventional principal component estimator. Last, asymptotic
equivalence of the GPCE and feasible GPCE (FGPCE) of the factor space is established.
Session D2 - Non-Linear Models 2
Unified estimation of panel regression models with simultaneous spatial and dynamic disturbances
Lung-fei Lee (Ohio State University); Jihai Yu (Peking University)
Abstract: This paper considers a quasi-maximum likelihood estimation for a linear panel data model with time and individual fixed
effects along with incidental trends, where the disturbances have dynamic and spatial correlations which might be spatially
nonstationary. Instead of using different estimation methods depending on whether the disturbance process is stable, spatial
cointegrated, or explosive, we propose to use "spatial differencing" on all variables in the regression equation as a data
transformation, which may eliminate unstable or explosive components in order to achieve a robust estimator. Additionally, we use
second difference to eliminate the individual effects and incidental trends. The estimates of regression parameters are
asymptotically centered normal. For the estimates of parameters of the disturbance process, when is relatively larger than
,
which is the effective sample size for each period after the transformation, the estimators are
consistent and asymptotically
centered normal; when T is asymptotically proportional to , the estimators are
consistent and asymptotically normal, but
the limit distribution may not be properly centered; when is relatively smaller than , the estimators are consistent with rate T
and have a degenerate limit distribution. We also propose a bias correction for these estimates. We show that when grows faster
than
, the correction will asymptotically eliminate the bias and yield a centered confidence interval.
Binary Response Models for Repeated Cross Sections
David Pacini (University of Bristol)
Abstract: We investigate the identification and estimation of the coefficients in a semiparametric single-index dynamic binary
response model when data are available from repeated cross sections instead of panels. The innovation is to allow for time-varying
variables among the regressors. This innovation is relevant because time-varying regressors are present in most applications. We
make two contributions. The first contribution is to provide a set of conditions sufficient for the point identification of the
coefficients entering the single-index. These conditions include the existence of an independent continuous regressors with large
support, and the existence of a time-invariant instrumental variable. We show that, under the mantained identification conditions,
the coefficients of interest are point identified by a conditional moment equality containing an unknown function. The second
contribution is to introduce a two-step semiparametric estimator of the coefficients of interest, and give asymptotic results to justify
its use in practice. The estimator is a minimizer of an U-process indexed by finite and infinite dimensional parameters. To the best
of our knowledge, the statistical properties of this sort of estimator have not been explored in the literature. We establish conditions
sufficient for its consistency and asymptotic normality. Monte Carlo exercises indicate that the proposed estimator performs well in
finite sample with respect to existing two-step estimators.
Estimation of some nonlinear panel data models with both time-varying and time-invariant explanatory variables
Bo Honore (Princeton University); Michaela Kesina (ETH Zurich)
Abstract: So-called fixed effects approach to estimating panel data models suffers from the limitation that it is not possible to
estimate the coefficients on explanatory variables that are time-invariant. This is in contrast to a random effects approach which
makes much stronger assumptions on the relationship between the explanatory variables and the individual specific effect. In a
linear model, it is possible to obtain the best of both worlds by making random effects type assumptions on the time-invariant
explanatory variables, while maintaining the flexibility of a fixed effects approach when it comes to the time--varying covariates.
See for example Hausman and Taylor (1981). This paper will make an attempt of doing the same for some popular nonlinear
models.
Variable coefficient binary choice panel data models: comparing Pooled and Mean Group estimators
Jaya Krishnakumar (University of Geneva); Laurent Pauwels (University of Sydney)
Abstract: This paper formulates a general random coefficient binary choice panel data model assuming all coefficients to be
random and derives the maximum likelihood estimators for the coefficient means. It aims to compare the large sample properties of
the pooled maximum likelihood estimator and Pesaran and Smith (1995) mean group estimator in estimating these random
coefficient models with a binary dependent variable. This paper looks into the behaviour of the predicted probabilities and marginal
effects as they are the key quantities of interest in such model. It turns out that the pooled maximum likelihood estimator is more
efficient than the mean group estimator, which is also verified by simulations. A series of Monte Carlo simulations investigate the
properties of the estimators as well as the predicted probabilities and marginal effects for different cross-section (N) and time-series
(T) sizes.
Session D3 - Prices
The Geography of Consumer Prices
Attila Ratfai (Central European University); Adam Reiff (National Bank of Hungary)
Abstract: We argue that the underlying width of the border in international price determination is a trivial fraction of the
corresponding Engel and Rogers (1996) reduced form estimate. We develop a two-country, multi-region, dynamic, stochastic
equilibrium model of monopolistic competition with costly price adjustment and cross-location shopping. The optimal price is
proportional to a weighted average of market prices, with weights negatively related to shopping costs. We calibrate structural
distance and border parameters to a unique panel of store-level prices, and conclude that price adjustment costs directly account for
about a quarter of the reduced form border width.
The Dynamics of Gasoline Prices: Evidence from Daily French Micro Data
Erwan Gautier (Université de Nantes); Ronan Le Saout (ENSAE)
Abstract: Using millions of individual gasoline prices collected at a daily frequency, we examine the speed at which market refined
oil prices are transmitted to consumer liquid fuel prices. We find that on average gasoline prices are modified once a week and the
distribution of price changes displays a M-shape as predicted by a menu-cost model. Using a reduced form state-dependent pricing
model with time-varying random thresholds, we find that the degree of pass through of wholesale prices to retail gasoline prices is
on average 0.77 for diesel and 0.67 for petrol. The duration for a shock to be fully transmitted into prices is about 10 days. There is
no significant asymmetry in the transmission of wholesale price to retail prices.
Not an average story: Asymmetric price transmission in the Hungarian gasoline retail market
Gábor Koltay (European Commission)
Abstract: The paper contributes to the disaggregated evidence about asymmetric price transmission. It studies how station-level
retail prices respond to wholesale price changes in the Hungarian gasoline market. The estimates show that although retail price
changes are almost symmetric on average, there is a subset of stations that follow an asymmetric pricing strategy. Having a closer
look at station characteristics reveals that asymmetric pricing is a brand property and that these brands have small market share
(below 10%) and are not vertically integrated. Other observables, like the number or the types of competitors do not explain the
asymmetric retail price response. These results imply that in the same local market there are firms that price symmetrically and
firms that price asymmetrically. This finding does not support collusion and search based explanations of asymmetric price
transmission, because these are based on market level interactions among firms and consumers. Instead, it points towards the role
of adjustment costs as an explanation for asymmetric retail price responses. Moreover, the result that the number and the types of
competitors does not explain asymmetric pricing lends additional support to the claim that pricing asymmetry does not necessarily
imply collusive behavior.
What's up? Patterns of Microeconomic Price Adjustment in France Before and During the Crisis
Nicoletta Berardi (Banque de France); Erwan Gautier (Université de Nantes); Hervé Le Bihan (Banque de France)
Abstract: This paper investigates the patterns of price adjustment at the microeconomic level, based on a large dataset of retail
prices that covers the current `great recession' episode. Overall, patterns of micro price adjustments are found to be in line with
those observed in the pre-crisis environment. The recent period has however witnessed an increase in the frequency of price
changes as well as a decline in the average size of price changes. These patterns, along with those of the cross-section of the
distribution of price changes, such as its peakedness, raise some challenges for the current models of price stickiness used for
monetary policy analysis.
Session D4 - Time Series Panels
Factor Augmented Autoregressive Distributed Lag Models
Serena Ng (Columbia University); Dalibor Stevanovic (Université du Québec à Montréal)
Abstract: This paper proposes a factor augmented autoregressive distributed lag (FADL) framework for analyzing the dynamic
effects of common and idiosyncratic shocks. We first estimate the common shocks from a large panel of data with a strong factor
structure. Impulse responses are then obtained from an autoregression augmented with a distributed lag of the estimated common
shocks. The approach has three distinctive features. First, identification restrictions, especially those based on recursive or block
recursive ordering are very easy to impose. Second, the dynamic response to the common shocks can be constructed for variables
not necessarily in the panel. Third, the restrictions imposed by the factor model can be tested. The relation to other identification
schemes used in the FAVAR literature is discussed. The methodology is used to study the effects of monetary policy shocks.
Bias Reduction under Dependence, in a Nonlinear and Dynamic Panel Setting: The Case of GARCH Panels
Cavit Pakel (University of Oxford)
Abstract: In nonlinear dynamic panels where the time-series dimension, T, is small relative to the cross-section dimension, N, fixed
effect models are subject to the incidental parameter bias. Considering a general setting where dependence across both T and N is
allowed, I use the integrated likelihood method to characterise this bias and obtain bias-reduced estimators. Under large-T, large-N
asymptotics, I show that time-series dependence leads to an extra incidental parameter bias term, which is not present in the iid
case. Moreover, due to cross-section dependence, a second type of bias emerges, the magnitude of which depends on the level of
dependence. Likelihood-based analytical expressions are provided for both terms. I then utilise these results to fit GARCH models
using a panel structure. Monte Carlo analysis reveals that the proposed method successfully fits GARCH with little bias and no
increase in variance using 150-200 time-series observations, compared to around 1,000-1,500 observations required for successful
GARCH estimation by standard methods. Simulation results further indicate that the effect of cross-section dependence on bias is
negligible, although it leads to higher estimator variance. Finally, I consider two empirical illustrations; an analysis of monthly
hedge fund volatility characteristics and a test of predictive ability using daily stock volatility forecasts.
IV-Based Cointegration Testing in Dependent Panels with Time-Varying Variance
Matei Demetrescu (University of Bonn); Christoph Hanck (Rijksuniversiteit Groningen); Adina Tarcolea (Goethe University
Frankfurt)
Abstract: While the limiting null distributions of cointegration tests are invariant to a certain amount of conditional
heteroskedasticity as long as global homoskedasticity conditions are fulfilled, they are certainly affected when the innovations
exhibit time-varying volatility. Worse yet, distortions from single units accumulate in panels, where one must anyway pay special
attention to dependence among cross-sectional units, be it time-dependent or not. To obtain a panel cointegration test robust to both
global heteroskedasticity and cross-unit dependence, we start by adapting the nonlinear instruments method proposed for the
Dickey-Fuller test by Chang (J Econometrics 110, 261--292) to an error-correction testing framework. We show that IV-based
testing of the null of no error-correction in individual equations results in asymptotic standard normality of the test statistic as long
as the t-type statistics are computed with White heteroskedasticity-consistent standard errors. Remarkably, the result holds even in
the presence of endogenous regressors, irrespective of the number of integrated covariates, and for any variance profile.
Furthermore, a test for the null of no cointegration-in effect, a joint test against no error correction in any equation of each unitretains the nice properties of the univariate tests. In panels with fixed cross-sectional dimension, both types of test statistics from
individual units are shown to be asymptotically independent even in the presence of correlation or cointegration across units,
leading to a panel test statistic robust to cross-unit dependence and unconditional heteroskedasticity. The tests perform well in
panels of usual dimensions with innovations exhibiting variance breaks and a factor structure.
On the Applicability of the Sieve Bootstrap in Time Series Panels
Stephan Smeekes (Maastricht University); Jean-Pierre Urbain (Maastricht University)
Abstract: In this paper we investigate the validity of the univariate autoregressive sieve bootstrap applied to time series panels
characterized by general forms of cross-sectional dependence, including but not restricted to cointegration. Using the final
equations approach we show that while it is possible to write such a panel as a collection of infinite order autoregressive equations,
the innovations of these equations are not vector white noise. This causes the univariate autoregressive sieve bootstrap to be invalid
in such panels. We illustrate this result with a small numerical example using a simple bivariate system for which the sieve
bootstrap is invalid, and show that the extent of the invalidity depends on the value of specific parameters. We also show that
Monte Carlo simulations in small samples can be misleading about the validity of the univariate autoregressive sieve bootstrap. The
results in this paper serve as a warning about the practical use of the autoregressive sieve bootstrap in panels where cross-sectional
dependence of general from may be present.
Session D5 - Econometric Theory 2
Nonparametric estimation of finite mixtures
Stephane Bonhomme (CEMFI); Koen Jochmans (Sciences Po); Jean-Marc Robin (Sciences-Po)
Abstract: The aim of this paper is to provide simple nonparametric methods to estimate finite-mixture models from data with
repeated measurements. Three measurements suffice for the mixtures to be fully identified and so our approach can be used even
with very short panel data. We provide distribution theory for estimators of the number of mixture components, the mixing
proportions, as well as of the mixture distributions and various functionals thereof. These estimators are found to perform well in a
series of Monte Carlo exercises. We apply our techniques to document heterogeneity in log annual earnings using PSID data
spanning the period 1969-1998.
A strategy to reduce the count of moment conditions in panel data GMM
Maria Elena Bontempi (Università degli Studi di Bologna); Irene Mammi (University of Bologna)
Abstract: The problem of instrument proliferation and its consequences (overfitting of endogenous variables, bias of GMM
estimates, weakening of Sargan/Hansen test) are well known. The literature provides little guidance on how many instruments is
too many. It is common practice to report the instrument count and to test the sensitivity of results to the use of more or fewer
instruments. Strategies to alleviate the instrument proliferation problem are the lag depth truncation and/or the collapse of the
instrument set (the latter being an horizontal squeezing of the instrument matrix). However such strategies involve either a certain
degree of arbitrariness (based on the ability and the experience of the researcher) or of trust in the restrictions implicitly imposed
(and hence untestable) on the instrument matrix. The aim of the paper is to introduce a new strategy to reduce the instrument count.
The technique we propose is statistically founded and purely data-driven and, as such, it can be considered a sort of benchmark
("neutral") solution to the problem of instrument proliferation. We apply the principal component analysis (PCA) on the instrument
matrix and exploit the PCA scores as the instrument set for the panel GMM estimation. Through extensive Monte Carlo
simulations, under alternative characteristics of persistence of the endogenous variables, we compare the performance of the
Difference GMM and System GMM estimators when lag truncation, collapsing and our principal component-based IV reduction
(PCIVR henceforth) are applied to the instrument set. Results show that PCIVR is a promising strategy of instrument reduction.
Efficient GMM Estimation with a General Missing Data Pattern
Chris Muris (Simon Fraser University)
Abstract: This paper considers GMM estimation from a random sample of incomplete observations. For each observation, certain
components of the moment function may be unavailable. We propose an estimator for an arbitrary set of regular moment
conditions and a general missing data pattern. The estimator is consistent and asymptotically efficient under an assumption that is
weaker than missing completely at random. It can be interpreted as the optimal linear combination of subsample GMM estimators.
Because of this linearity, the computational burden and the small-sample performance of the estimator are comparable to the fulldata estimator. We also propose an inverse probability weighted version of the estimator that is consistent when selection is on
observables. Applications to multivariate mean estimation, instrumental variable estimation, and dynamic panel data estimation
demonstrate the efficiency gain with respect to existing missing data methods. We also discuss how the results can be used to
optimize data collection for measuring consumer confidence.
A Robust Hausman-Taylor Estimator
Badi Baltagi (Syracuse University); Georges Bresson (University of Paris II / Sorbonne Universities)
Abstract: This paper suggests a robust Hausman and Taylor (1981) estimator, hereafter HT, that deals with the possible presence
of outliers. This entails two modifications of the classical HT estimator. The first modification uses the Bramati and Croux (2007)
robust Within MS estimator instead of the Within estimator in the first stage of the HTestimator. The second modification uses the
robust Wagenvoort and Waldmann (2002) two stage generalized MS estimator instead of the 2SLS estimator in the second step of
the HT estimator. Monte Carlo simulations show that, in the presence of vertical outliers or bad leverage points, the robust HT
estimator yields large gains in MSE as compared to its classical Hausman-Taylor counterpart. We illustrate this robust version of
the Hausman-Taylor estimator using an empirical application.
Session D6 - Forecasting
Microdata Imputations and Macrodata Implications: Evidence from the Ifo Business Survey
Christian Seiler (Ifo Institute); Christian Heumann (University of Munich)
Abstract: A widespread method for now- and forecasting economic macro level parameters such as GDP growth are survey-based
indicators which contain early information in contrast to official data. But surveys are commonly affected by nonresponding units
which can produce biases if these missing values cannot be regarded as missing at random. As many papers examined the effect of
nonresponse in individual or household surveys, only less is known in the case of business surveys. So, literature leaves a gap on
this issue. For these reason, we analyse and impute the missing observations in the Ifo Business Survey, a large business survey in
Germany. The most prominent result of this survey is the Ifo Business Climate Index, a leading indicator for the business cycle
development in Germany. To reflect the underlying latent data generating process, we compare different imputation approaches for
longitudinal data. After this, the microdata are aggregated and the results are compared with the original indicators to evaluate their
implications on the macro level. Finally, we show that the bias is minimal and ignorable.
Prediction in an Unbalanced Nested Error Component Panel Data Model
Badi Baltagi (Syracuse University); Alain Pirotte (University of Paris II)
Abstract: This paper derives the Best Linear Unbiased Predictor for an unbalanced nested error component panel data model. This
predictor is useful in many econometric applications that are usually based on unbalanced panel data and have a nested
(hierarchical) structure. Examples include predicting student performance in a class in a school, or house prices in a neighborhood
in a county or a state. Using Monte Carlo simulations, we show that this predictor is better in root mean square error performance
than the usual
fixed or random effects predictors ignoring the nested structure of the data.
Banking Crises, Early Warning Models, and Effciency
Pavlos Almanidis (Ernst & Young-Toronto); Robin Sickles (Rice University)
Abstract: In this paper we propose a general model that combines the Mixture Hazard Model (MHM) of Farewell (1977, 1982)
with the Stochastic Frontier Model (SFM) to investigate the main determinants of the probability and time to failure of a panel of
U.S. commercial banks during the financial distress that began in August of 2007. Unlike the standard hazard model which would
assume that all banks in the sample would eventually experience the event (failure), the MHM model distinguishes between healthy
(long-term survivors) and at-risk banks. On the other hand, SFM provides a measure of the performance of banks which reflects
management quality and potentially plays a key role in their failure, conditional on the usual financial ratios and other
macroeconomic, structural, and geographical variables that we employ. We consider both continuous-time semi-parametric and
discrete-time mixture hazard models which are separately or jointly estimated with the stochastic frontier specification. Joint
estimation allows not only the performance to affect the probability and time to failure, but also the former to affect the latter. The
estimation of these models is carried out via expectation-maximization (EM) algorithm and simulated maximum likelihood (SML)
method due to the incomplete information regarding the identity of at-risk banks and to the fact that integration must be carried out
in evaluating the likelihood function. In- and out-of-sample predictive accuracy of these models is investigated in order to assess
their potential to serve as early warning tools for regulatory authorities, academic practitioners, and bank insiders.
Session E1 - Dynamic Models 3
QML Estimation of Dynamic Panel Data Models with Spatial Errors
Liangjun Su (Singapore Management University); Zhenlin Yang (Singapore Management University)
Abstract: We propose quasi maximum likelihood (QML) estimation of dynamic panel models with spatial errors when the crosssectional dimension n is large and the time dimension T is fixed. We consider both the random effects and fixed effects models and
derive the limiting distributions of the QML estimators under different assumptions on the initial observations. We propose a
residual-based bootstrap method for estimating the standard errors of the QML estimators. Monte Carlo simulation shows that both
the QML estimators and the bootstrap standard errors perform well in finite samples under a correct assumption on initial
observations, but not so well when this assumption is not met.
GMM-based inference in the AR(1) panel data model for parameter values where local identification fails
Edith Madsen (Technical University of Denmark)
Abstract: We are interested in making inference on the AR parameter in an AR(1) panel data model when the time-series
dimension is fixed and the cross-section dimension is large. We consider a GMM estimator based on the second order moments of
the observed variables. It turns out that when the AR parameter equals unity and certain restrictions apply to the other parameters in
the model then local identification fails. We show that the parameters and in particular the AR parameter can still be estimated
consistently but at a slower rate than usual. Also we derive the asymptotic distribution of the parameter estimators which turns out
to be non-standard. The properties of this estimator of the AR parameter are very different from those of the widely used ArellanoBond GMM estimator which is obtained by taking first-differences of the variables and then use lagged levels as instruments. It is
well-known that this estimator performs poorly when the AR parameter is close to unity. The reason for this difference between the
two estimators is that with the Arellano-Bond moment conditions global identification fails for the specific values of the
parameters considered here whereas with the non-linear GMM estimator that uses all information from the second order moments
only local identification fails. We illustrate the results in a simulation study.
Bias Reduction in Multilevel Dynamic Panels with Small T: An Application to the Dynamics of Corn Supply
Nathan Hendricks (Kansas State University); Aaron Smith (UC Davis)
Abstract: We propose the use of a grouped coefficients estimator to reduce the bias of dynamic panels that have a multilevel
structure to the coefficient and factor loading heterogeneity. If groups are chosen such that the within-group heterogeneity is small,
then the grouped coefficients estimator can lead to a substantial bias reduction compared to fixed effects and Arellano-Bond
estimators. We also compare the magnitude of the bias of panel estimators with individual versus aggregate data and show that the
magnitude of the bias also depends on the proportion of the heterogeneity that is within groups. In an application to estimating corn
acreage response to price, we find that the grouped coefficients estimator gives reasonable results. Fixed effects and Arellano-Bond
estimates of the coefficient on the lagged dependent variable appear to be severely biased with county-level data. In contrast, if we
randomly assign the fields to groups and aggregate within the random groups, then pooled OLS of the randomly aggregated data
gives a reasonable estimate of the coefficient on the lagged dependent variable.
On the use of the Arellano-Bond estimator
Christoph Hanck (University of Groningen); Laura Spierdijk (University of Groningen); Tom Wansbeek (University of Groningen)
Abstract: In this paper, we consider the consequences when a dynamic panel data model is estimated with instruments whose
validity depend on the error terms being uncorrelated, while they are in fact correlated. In particular, the long-run coefficient is
overestimated. Results from a simple model suggest that the overestimate may be quite severe. For the same model, we derive
results for estimation by a method that hedges against such inconsistency. It apprears that this method need not do worse.
Session E2 - Production and Productivity 2
Does China overinvest? Evidence from a panel of Chinese firms
Sai Ding (University of Glasgow); Alessandra Guariglia (Durham University); John Knight (University of Oxford)
Abstract: This paper uses a panel dataset of more than 100,000 firms over the period of 2000-07 to assess the extent to which
Chinese firms overinvest. We find that corporate investment in China has become increasingly efficient over time, which suggests
that overinvestment has been declining. However, making use of direct measures of overinvestment, we find evidence of this
phenomenon for all types of firms. The free cash flow hypothesis provides a good explanation for China’s overinvestment in the
collective and private sectors, whereas in the state sector, overinvestment is attributable to the poor screening and monitoring of
enterprises by banks.
Import Competition, Domestic Regulation and Firm-Level Productivity Growth in the OECD
Sarra Ben Yahmed (Université de la Méditerranée); Sean Dougherty (OECD)
Abstract: This paper examines how import penetration affects firms’ productivity growth taking into account the heterogeneity in
firms’ distance to the efficiency frontier and country differences in product market regulation. Using firm-level data for a large
number of OECD countries, the analysis reveals non-linear effects of both sectoral import penetration and de jure product market
regulation measures depending on firms’ positions along the global distribution of productivity levels. The heterogeneous effects of
international competition and domestic product market regulation on firm-level productivity growth are consistent with a neoSchumpeterian view of trade and regulation. Close to the technology frontier, import competition has a strongly positive effect on
firm-level productivity growth, with stringent domestic regulation reducing this effect substantially. However, far from the frontier,
neither import competition nor its interaction with domestic regulation has a statistically significant effect on firm-level
productivity growth. The results suggest that insufficient attention has been made in the trade literature to within-firm productivity
growth.
The Evolution of Cost-Productivity and Efficiency Among U.S. Credit Unions
David Wheelock (Federal Reserve Bank of St. Louis); Paul Wilson (Clemson University)
Abstract: Advances in information-processing technology have significantly eroded the advantages of small scale and proximity to
customers that traditionally enabled community banks and other small-scale lenders to thrive. Nonetheless, U.S. credit unions have
experienced increasing membership and market share, though consolidation has reduced the number of credit unions and increased
their average size. We investigate the evolution of the efficiency and productivity of U.S. credit unions between 1989 and 2006
using a new methodology that benchmarks the performance of individual firms against an estimated order-α quantile lying "near"
the efficient frontier. We construct a cost analog of the widely-used Malmquist productivity index, and decompose the index to
estimate changes in cost and scale efficiency, and changes in technology, that explain changes in cost-productivity. We find that
cost-productivity fell on average across all credit unions but especially among smaller credit unions. Smaller credit unions
confronted an unfavorable shift in technology that increased the minimum cost required to produce given amounts of output. In
addition, all but the largest credit unions became less scale efficient over time.
Estimation of the Industry Production Function with Biased Technical Change: A Control Function Approach
Xi Chen (Université de Strasbourg)
Abstract: In this paper, I extend the Olley-Pakes (1996) estimation method to the CES production function with biased technical
change. The new semi-parametric approach allows consistent estimation of the degree of returns to scale, the elasticity of
substitution, and the bias in technical change. Identification of these parameters is achieved under the assumption that the data
generating process reflects not only technologies but also optimizing behavior of producers. Using data from U.S. manufacturing
industries over the period 1958-2005, I find strong evidence that industries are characterized by a production technology with the
elasticity of substitution below one and with significant biased technical progress.
Nonlinearities in productivity growth: A semi-parametric panel analysis
Theophile Azomahou (United Nations University –UNU MERIT and University of Maastricht); Bity Diene (University of
Auvergne, CERDI); Mbaye Diene (University Cheikh-Anta-Diop, CRES)
Abstract: In this paper, we use country panel data spanning over 1998-2008 for both develop and developing countries to study
nonlinearities in productivity growth when countries are close to the technology frontier. By emphasizing the crucial role of
nonlinearity in the productivity process, our study contributes to this literature in several aspects. The study delivers several key
results: i) While TFP growth is a decreasing mixing slope function of proximity to the highest TFP growth, it is increasing with
proximity to the world and US TFP growth. ii) The relation between TFP growth and human capital (measured by the percentage
of graduate students in higher education) displays an inverted U-shape form (res. U-shape) when the proximity to the highest TFP
growth is used (res. the proximity to US TFP growth). iii) Total staff in R&D has an inverted W-shape effect on TFP growth. iv)
The share of R&D expenditure funded by government and from abroad impact positively the growth of TFP. However, there is no
evidence of R&D expenditure funded by the business sector on TFP growth. v) International trade (measured by country openness)
has a positive effect on TFP growth. However, we do not find evidence that FDI impacts productivity growth. vi) The structural
form estimation shows a U-shape relation between TFP growth and the proximity to the world TFP growth. This finding seems to
reconcile the relations from two other proximity measures (decreasing for the proximity to the highest TFP growth and increasing
for the proximity to the TFP growth of USA). vii) Also, an increase in government spending in R&D has a greater impact on TFP
growth when the latter is low, and a smaller impact when TFP growth is already high reflecting a S-shape nonlinearity. viii) Last,
specification tests show that our semi-parametric models provide a better approximation of the data compared to the parametric
analogue, revealing a high degree of nonlinearity which governs the productivity growth process.
Session E3 - Cross-Section Dependence and Interactions
EC3SLS Estimator for a Simultaneous System of Spatial Autoregressive Equations with Random Effects
Badi Baltagi (Syracuse University); Ying Deng (Syracuse University)
Abstract: This paper derives a 3SLS estimator for a simultaneous system of spatial autoregressive equations with random effects
which can therefore handle endogeneity, spatial lag dependence, heterogeneity as well as cross equation correlation. This is done
by utilizing the Kelejian and Prucha (1998) and Lee (2003) type instruments from the cross-section spatial autoregressive literature
and marrying them to the error components 3SLS estimator derived by Baltagi (1981) for a system of simultaneous panel data
equations. Our Monte Carlo experiments indicate that, for the single equation spatial error components 2SLS estimators, there is a
slight gain in efficiency when Lee (2003) type rather than Kelejian and Prucha (1998) instruments are used. However, there is not
much difference in efficiency between these instruments for spatial error components 3SLS estimators.
GMM estimation of fixed effects dynamic panel data models with spatial lag and spatial errors
Pavel Cizek (Tilburg University); Jan Jacobs (University of Groningen); Jenny Ligthart (Tilburg University); Hendrik Vrijburg
(Erasmus University Rotterdam)
Abstract: We extend the three-step generalized methods of moments (GMM) approach of Kapoor et al. (2007), which corrects for
spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as
additional explanatory variables. Combining the extended Kapoor et al. (2007) approach with the dynamic panel data model GMM
estimators of Arellano and Bond (1991) and Blundell and Bond (1998) and specifying moment conditions for various time lags,
spatial lags, and sets of exogenous variables yields new spatial dynamic panel data estimators. We prove their consistency and
asymptotic normality for a large number of spatial units N and a fixed small number of time periods T. Monte Carlo simulations
demonstrate that the root mean squared error of spatially corrected GMM estimates (which are based on a spatial lag and spatial
error correction) is generally smaller than that of corresponding spatial GMM estimates in which spatial error correlation is
ignored. We show that the spatial Blundell-Bond estimators outperform the spatial Arellano-Bond estimators.
On bootstrapping panel factor series
Lorenzo Trapani (Cass Business School, London, UK)
Abstract: This paper studies the asymptotic validity of sieve bootstrap for nonstationary panel factor series. Two main results are
shown. Firstly, a bootstrap Invariance Principle is derived pointwise in i, obtaining an upper bound for the order of truncation of
.
the AR polynomial that depends on and T. Consistent estimation of the long run variances is also studied for (
Secondly, joint bootstrap asymptotics is also studied, investigating the conditions under which the bootstrap is valid. The extent of
cross sectional dependence which can be allowed for is investigated, showing that, in the presence of strong cross dependence,
consistent estimation of the long run variance (and therefore validity of the bootstrap) is no longer possible. The paper also
considers extensions to the case of a mixture of stationary and nonstationary common factors.
Who are the Voluntary Leaders? Experimental Evidence from a Sequential Contribution Game.
Phu Nguyen-Van (Universite de Strasbourg); Raphaële Préget (INRA); Marc Willinger (Lameta)
Abstract: We investigate the reasons why some individuals are willing to act as a leader while others prefer to act as a follower.
We rely on the methodology of Fischbacher et al. (2001) and Fischbacher and Gächter (2010) to identify subjects’ behavioral
types. We then link the propensity to act as a leader in a repeated public goods game to the elicited behavioral types. For a given
contribution round, the leader in a group is defined as the subject who voluntarily decides in the first place about his contribution.
The leader’s contribution is then reported publicly to the remaining group members, before they are requested to take their
contribution decision simultaneously. Our main findings is that leaders emerge in almost all rounds, that conditional cooperators
are more likely to act as leaders than the free-riders, and leaders contribute more than followers.
Understanding Interactions in Social Networks and Committees: A small panel approach
Arnab Bhattacharjee (University of Dundee); Sean Holly (Cambridge University)
Abstract: While much of the panel literature on cross section dependence has focused on estimation of the regression coefficients
in the underlying model, estimation and inferences on the magnitude and strength of spillovers and interactions has been largely
ignored. At the same time, such inferences are important in many applications, not least because they have structural interpretations
and provide useful inferences and structural explanation for the strength of any interactions. In this paper we propose GMM
methods designed to uncover underlying (hidden) interactions in social networks and committees. Special attention is paid to the
interval censored regression model. Small sample performance is examined through a Monte Carlo study. Our methods are applied
to a study of committee decision making within the Bank of England's monetary policy committee.
Session E4 - Market Structure
Rent Building, Rent Sharing - A Panel Country-Industry Empirical Analysis
Philippe Askenazy (Paris School of Economic-CNRS); Gilbert CETTE (Banque de France); Paul Maarek (Banque de France and
Université de Cergy-Pontoise)
Abstract: Through panel estimates using OECD country-industry statistics, this paper aims to clarify the determinants of rent
creation and the mechanisms of rent sharing, and the role of market regulations in these processes. It uses a panel database of 4,136
observations, comprising industry-level data on 17 OECD countries over the period 1988 to 2007. This dataset merges the STAN
database and regulation indicators, both compiled by the OECD. Our approach presents three original features. First, the empirical
analysis is carried out in two steps. The first explains the rent creation process. For each country-industry-year observation, the size
of rents, measured by the value added price relative to the GDP price, is assumed to depend solely on direct anti-competitive
regulations on services and goods. The second step explains the rent sharing process. The second original feature is that three
destinations of rents are distinguished for each country-industry-year observation: upstream industries, capital and labour. Finally,
the cross-country-industry analysis makes it possible to estimate more complex relations than at the country data level. The main
empirical findings are as follows. Regarding the rent creation step, direct anti-competitive regulations are associated with a very
significant rise in rent size. Concerning the rent sharing step, the capital share in value added appears to i) increase with rent size,
decrease with anti-competitive regulation in upstream sectors and increase with the industry specific output gap; ii) decrease with
the national output gap, increase with the national employment rate and decrease with employment protection regulation; iii)
increase with the interaction of rent size and the unemployment rate and decrease with the interaction of rent size and employment
protection regulations. These results confirm the existence of three destinations for rents (labour remuneration, capital
remuneration and upstream industries). They also show that the magnitude of each destination depends on the market power of its
beneficiary. All these results are robust to a variety of sensitivity checks.
Market Structure, Sunk Costs and Scope Economies in Pharmaceutical R&D
Laura Magazzini (University of Verona); Fabio Pammolli (IMT Lucca Institute of Advanced Studies); Massimo Riccaboni (IMT
Lucca Institute for Advanced Studies)
Abstract: This paper investigates the effect of sunk cost, market size, and scope economies on entry of new drugs in the
pharmaceutical industry. We analyse entry decisions of pharmaceutical companies at the submarket level of specific medical
indications. First, we estimate the sunk cost in R&D based on attrition rates in clinical trials. Next, we proxy the specificity of
R&D investments in a therapeutic market by considering the drugs exclusively targeted to the relevant indication. Finally, we test
an econometric model of the determinants of the number of new entrants at the submarket level. We find a positive relationship
between market size and firm numbers as well as a negative effect of R&D sunk costs on market entry. On the R&D side,
submarkets with larger R&D efforts attract more research, consistently with the escalation mechanism in pharmaceuticals (Sutton,
2007).
The Law of One Price and the Role of Market Structure
Mustafa Caglayan (University of Sheffield); Alpay Filiztekin (Sabanci University)
Abstract: This paper examines the role of market structure on the persistence of price deviations from the LOP using monthly
actual product prices of 47 items collected from three different types of markets in Istanbul over 1993:01-2008:12. After showing
the importance of market structure on the distribution of relative prices, we implement threshold autoregressive models. We find
significant differences in average threshold estimates across markets which we explain referring to differing menu costs in each
market. Yet, we find no differences in average half-life estimates across markets. We argue that this is due to low search costs in
Istanbul. Robustness checks verify our findings.
Job Matching on non separated Occupational Labour Markets
Michael Stops (Institute for Employment Research)
Abstract: This paper refers to analyses of matching processes on occupational labour markets. Up to now, all studies in this field
are based on the crucial assumption of separate occupational labour markets. I outline some empirical and theoretical
considerations that occupational markets are probably not completely separated. By using information about similarities of
occupational groups I construct an "occupational topology" and test a hypothesis of non-separated occupational labour markets
with OLS, Fixed Effects and Pooled Mean-Group models including cross-sectional dependency lags for regressors. The results
show considerable dependencies between similar occupational groups in the matching process. This has important implications for
estimating the matching elasticities of unemployed and vacancies, because the matching process is not only determined by the
unemployed and vacancies in the same occupational group but also by those in other occupational groups. Furthermore there are
indications that the returns to scale derived from the results of the pooled mean-group model are constant.
Variation in Monopsonistic Behavior Across Establishments: Evidence From the Indonesian Labor
Peter Brummund (Cornell University)
Abstract: Firms are able to behave monopsonistically when hiring workers because of frictions in the labor market. These frictions
have traditionally been thought of as moving costs that separate labor markets. More recent theoretical work has shown that
individual firms can have market power independent of the labor market due to other frictions, such as search frictions or
information asymmetries. However, current techniques for measuring market power are unable to separate the firm determinants of
market power from the market determinants. This paper proposes a new method for measuring monopsonistic behavior that yields
a firm-specific measurement. I apply this measure to the Indonesian manufacturing sector, where I argue labor market frictions are
more prevalent than in developed countries. To my knowledge, this is the first empirical evidence for monopsonistic behavior of
establishments in an emerging economy. I find that over half of the manufacturing establishments have a significant amount of
market power, with the median firm facing a labor supply elasticity of 0.60. I then show that individual establishment
characteristics explain more of the variation in monopsonistic behavior than the characteristics of the labor market in which the
establishment participates in.
Session E5 - R&D, Capital and Technical Change
Corporate investment and bank-dependent borrowers during the recent financial crisis
Andra Buca (ECB); Philip Vermeulen (ECB)
Abstract: This paper provides empirical evidence on the causal role of bank credit in explaining the
collapse in corporate investment during the recent financial crisis. Using annual balance sheet data over the period 2000-2009 for
Germany, France, Italy, Spain, Belgium and Portugal we compare the role of bank credit versus other types of credit for corporate
investment during different episodes. We distinguish between investment boom years, investment downturn years and the
investment collapse of 2009. We show that firm investment became highly sensitive to bank debt during the investment collapse of
2009. During the crisis, higher bank debt leverage of firms is associated with reduced investment. The effects we find are quite
sizeable. This contrasts with an absence of bank credit effects before the crisis and contrasts further with the absence of effects of
other credit during the crisis. The effects of bank debt are largest for small and medium sized firms. We also find evidence that the
effects are restricted to the South of Europe (Italy, Portugal, Spain). Our finding lends support to the view that banks credit supply
was restricted during the recent financial crisis leading to real effects.
The R&D Tax Credit in France: A first assessment of the 2008 Reform
Benoit Mulkay (Université de Montpellier 1); Jacques Mairesse (ENSAE and Maastricht University)
Abstract: The aim of this article is to evaluate the reform of the R&D tax credit (RTC) in France on R&D expenditures and on
innovation. Before 2008, the R&D tax credit was computed on an incremental basis, with an introduction of a tax credit on the
level of R&D since 2004. In 2008, the government has changed the R&D tax credit for a tax credit only on the amount of R&D
expenditure with a larger nominal rate. Our first results show that the reform is effective but slow: the immediate effect on R&D
by 1 % in 2008, and 4 % in 2009. Then the effect increases to a maximum at 19 % in 2012, but it goes down at long run to a 12 %
increase of R&D relative to the benchmark. The implicit multiplier of the RTC is the ratio of the change in R&D due to the reform
on the change in the total cost of the RTC for the public finance. The implicit multiplier increases during 5 years, and then it goes
down to its long run equilibrium of 0.7: at long run the cost of the reform is larger than the supplementary R&D because there is a
windfall effect on private R&D. There is an additionality effect on R&D with an implicit multiplier above 1 only between 4 and 6
years after the reform.
ICT Intermediates, Growth and Productivity Spillovers
Thomas Strobel (Ifo Institute for Economic Research)
Abstract: Recent pre-crisis growth accounting exercises attribute productivity growth accelerations to increased investments in
information and communication technologies (ICT), especially during the mid-1990s. EU-wide stylized facts about a growing USEU productivity gap are confirmed for Germany, particu-larly showing no substantially economy-wide effects from ICT for
German sectors. Tracing the effect from ICT during the period 1991-2005, this study takes a different view by expanding the
concept of value added to gross output including different types of intermediate inputs. The findings suggest that imported
intermediate inputs played a more dominating role in Germany than in the US, particularly imported non-ICT and ICT materials. In
the US, main driving forces were domestically-produced non-ICT services and ICT materials, even though imported ICT materials
were on the upraise post 1995. Moreover, there have been strong spillover effects from increasing domestically-produced ICT
materials in German TFP growth, while US TFP growth originated from increasing imported ICT materials. It will be argued that
these different productivity effects stem from different functions of ICT in the production process. However, German-US TFP
growth differentials during 1991 to 2000 are ex-plained to a great extent by strong US TFP growth in the Electrical & Electronic
Machinery sector.
Intensité de l’investissement privé en R&D dans les pays de l’OCDE : Impact et complémentarité des mesures de soutien
financier
Benjamin Montmartin (Jean Monnet University of Saint-Etienne)
Abstract: Les politiques de soutien financier à la R&D se sont multipliées depuis les années 80 dans les pays de l’OCDE avec une
utilisation croissante des mesures fiscales. Cependant, il existe assez peu d’études mesurant l’impact macroéconomique de ces
mesures sur l’investissement privé en R&D. L’objectif de cet article est d’analyser l’impact et la complémentarité des politiques
internes de soutien financier à la R&D mais aussi l’influence des politiques externes sur l’intensité de la R&D financée par le
secteur privé. En utilisant une base de données couvrant 25 pays de l’OCDE sur la période 1990-2007, nos estimations en panel
dynamique montrent que seules les politiques internes de soutien financier indirect (incitations fiscales) influencent
significativement l’intensité de la R&D privée. Si les instruments de soutien direct (subventions et prêts) n’ont pas d’impact
significatif, il apparaît que leur renforcement nuirait à l’efficacité des mesures indirectes. Alors que la R&D publique externe
semble dynamiser l’investissement privé en R&D, les politiques extérieures de soutien financier (direct et indirect) ne montrent pas
d’influence significative.
A General Model of Technical Change with an Application to OECD Countries
Almas Heshmati (Korea University); Subal Kumbhakar (SUNY Binghamton (University of Stavanger, Norway)
Abstract: In the neoclassical production functions model technical change (TC) is assumed to be exogenous and it is specified as a
function of time. However, some exogenous external factors other than time can also affect the rate of TC. In this paper we model
TC via a combination of time trend (purely non-economic) and other observable exogenous factors, which we call technology
shifters (economic factors). We use several composite technology indices based on appropriate combinations of the external
economic factors which are indicators of different aspects of technology. These technology indices are embedded into the
production function in such a way that they can complement to different inputs. By estimating the generalized production function,
we get estimates of TC which is decomposed TC into a pure time component as well as several producer specific external
economic factors. Furthermore, the technology shifters allow for non-neutral and biased shifts in TC. We also consider a simple
model in which the technology shifters are aggregated into one single index. The empirical model uses panel data on OECD,
accession and enhanced engagement countries observed during 1980-2006.
Session E6 - Specification of Panel Models
On the Role of Time in Nonseparable Panel Data Models
Stefan Hoderlein (Boston College); Yuya Sasaki (Brown University)
Abstract: This paper contributes to the understanding of the source of identification in panel data models. Recent research has
established that few time periods suffice to identify interesting structural effects in nonseparable panel data models even in the
presence of complex correlated unobservables, provided these unobservables are time invariant. A communality of all of these
approaches is that they point identify effects only for subpopulations. In this paper we focus on average partial derivatives and
continuous explanatory variables. We elaborate on the parallel between time in panels and instrumental variables in cross sections
and establish that point identification is generically only possible in specific subpopulations, for finite T. Moreover, for general
subpopulations, we provide sharp bounds. Finally, we show that these bounds converge to point identification as T tends to infinity
only. We systematize this behavior by comparing it to increasing the number of support points of an instrument. Finally, we apply
all of these concepts to the semiparametric panel binary choice model and establish that these issues determine the rates of
convergence of estimators for the slope coefficient.
Discrete Heterogeneity Patterns in Panel Data
Stéphane Bonhomme (CEMFI); Elena Manresa (CEMFI)
Abstract: We propose a panel data estimator that leaves the relationship between observables and unobservables unrestricted,
while allowing for flexible time-varying patterns of heterogeneity. Our
approach restricts the support of unobserved heterogeneity, effectively assuming that individual
units belong to a small number of groups. The “grouped fixed-effects” estimator that this paper
introduces is shown to be higher-order unbiased as N and T tend to infinity, under conditions
that we characterize. As a result, inference is not affected by the fact that group membership is
estimated. We apply our approach to study the link between income and democracy, while allowing
for permanent and time-varying country-specific heterogeneity. The results differ significantly from
approaches that assume that country heterogeneity does not vary over time.
The Formulation and Estimation of Random Effects Panel Data Models of Trade
Laszlo Matyas (Central European University); Cecilia Hornok (Central European University); Daria Pus (Central European
University)
Keywords: panel data, multidimensional panel data, random effects, error components model, trade model, gravity model
Abstract: The paper introduces for the most frequently used three-dimensional panel data sets several random effects model
specifications. It derives appropriate estimation methods for the balanced and unbalanced cases. An application is also presented
where the bilateral trade of 20 EU countries is analysed for the period 2001-2006. The differences between the fixed and random
effects specifications are highlighted through this empirical exercise.
Transformations for general variance covariance structures in a two-way error component model
Carlos de Porres Ortiz de Urbina (University of Geneva); Jaya Krishnakumar (University of Geneva)
Abstract: This paper examines general variance-covariance structures for the specific effects and the overall error term in a twoway error component (EC) model. So far panel data literature has only considered these general structures in a one-way model and
followed the approach of a Cholesky-type transformation to bring the model back to a `classical' one-way EC case. In this paper,
we first show that in a two-way setting it is impossible to find a Cholesky-type transformation when the error components have a
general variance-covariance structure (which includes autocorrelation). Then we propose solutions for this general case using the
spectral decomposition of the variance components and give a general transformation leading to a block-diagonal structure which
can be easily handled. The results are obtained under some general conditions on the matrices involved which are satisfied by most
commonly used structures. Our solution partially corroborates the conjecture of Magnus and Murius (2010) that any two-way EC
has to be expressed as a one-way for obtaining the spectral decomposition, but only at a second stage after an initial transformation
of the model. Our approach is also different from that of Brou et al. (2011) and unlike the latter provides explicit solutions for the
inverse and the determinant of the variance covariance structures. Thus our results provide a general framework for introducing
new variance-covariance structures in a panel data model. This is illustrated by taking some interesting special cases and showing
how the spectral decomposition can be derived using our results.
Revisiting the Statistical Foundations of Panel Data Modeling
Aris Spanos (Virginia Tech)
Abstract: Despite the impressive developments in panel data modeling, the statistical foundations of such models are rather weak
in so far that they are inadequate for securing the reliability and precision of inference. In statistical induction we learn from data
about phenomena of interest when we employ reliable and incisive inference procedures. When one invokes either untested
probabilistic assumptions, or/and broad (including nonparametric) premises, one has to rely on crude approximations (asymptotic)
for evaluating the relevant error probabilities. This invariably leads to imprecise inference of unknown reliability because one does
not know how closely the actual error probabilities approximate the assumed nominal ones, or how effective the inference
procedures are! The primary objective of the paper is to revisit these probabilistic foundations with a view to: (a) recast the error
assumptions in terms of the probabilistic structure of the observable processes underlying the data, (b) provide a complete and
internally consistent set of testable probabilistic assumptions for several statistical models for panel data, and (c) propose pertinent
interpretations for the individual-specific (fixed or random) and time-specific effects. It is shown that the current interpretations of
the individual-specific effects (fixed or random) need to be reconsidered in light of the implicit statistical parameterizations in
terms of the observable stochastic processes involved. This provides a more appropriate framework for securing learning from
panel data by bringing out the neglected facets of empirical modeling which include specification, misspecification testing and
respecification.
LIST OF PARTICIPANTS
Last Name
First Name
Institution
ABDALLA
Suliman
King Saud University
ALEKSANYAN
Lilia
Banque de France
AMARAKOON
Bandara
United Nations Development Programme
AQUARO
Michele
Tilburg University
ARRONDEL
Luc
Banque de France
AVOUYI-DOVI
Sanvi
Banque de France
AZOMAHOU
Theophile
UNU-MERIT and Maastricht University
BAILEY
Natalia
University of Cambridge
BALAZSI
Laszlo
Central European University
BALTAGI
Badi
Syracuse University
BANERJEE
Anindya
Banque de France
BAUM
Christopher
Boston College
BEAU
Denis
Banque de France
BEN SALEM
Mélika
Centre d'Etudes de l'Emploi - PSE
BEN YAHMED
Sarra
Université de la Méditerranée
BERARDI
Nicoletta
Banque de France
BERTHOU
Antoine
Banque de France
BERTUSSI
Geovana
Universidade de Brasilia - UnB
BINDER
Michael
Goethe University Frankfurt
BONLEU
Antoine
Université de la Méditerranée
BONTEMPI
Maria Elena
Università degli Studi di Bologna
BOURGEON
Pauline
Banque de France
BRESSON
Georges
Université Paris II / Université Sorbonne
BRUMMUND
Peter
Cornell University
CAGLAYAN
Mustafa
University of Sheffield
CANER
Mehmet
North Carolina State University
CARLUCCIO
Juan
Banque de France
CERRATO
Mario
University of Glasgow
CETTE
Gilbert
Banque de France
CHANG
Yoosoon
Indiana University
CHEN
Xi
University of strasbourg
CHEN
Linna
Xiamen University
CHENG
Gong
Banque de France
CHO
Sungjin
Seoul National University
CHOI
In
Sogang University
CHOJNA-DUCH
Elzbieta
National Bank of Poland
CHVOSTA
Jan
SAS
CIZEK
Pavel
Tilburg University
DE PORRES ORTIZ DE URBINA Carlos
University of Geneva
DE BANDT
Olivier
ACP-French Supervising Authority
DEBBICH
Majdi
Banque de France
DEMETRESCU
Matei
University of Bonn
DING
Sai
University of Glasgow
DOAN
Thi Hong Thinh
Université de la Méditerranée
DUGUET
Emmanuel
Université Paris Est - ERUDITE
DURAND
Christian
Banque de France
EHRHART
Hélène
Banque de France
EVDOKIMOV
Kirill
Princeton University
FANG
Ying
Xiamen University
FELTKAMP
Vincent
Maastricht School of Management
FERRARA
Laurent
Banque de France
FLORES-LAGUNES
Alfonso
State University New York - Binghamton
FOUGERE
Denis
CREST - ENSAE & Banque de France
FRAISSE
Henri
ACP-French Supervising Authority
GARNERO
Andrea
ENS, PSE and Univ. Libre de Bruxelles
GAUTIER
Erwan
Université de Nantes
GHOSH
Atish
International Monetary Fund
GOURIEROUX
Christian
University of Toronto and CREST
GUERIN
Selen
Vrije Universiteit Brussel
GUNNES
Trude
Statistics Norway
GUYOMARD
Hervé
INRA
HAAN
Peter
DIW
HANCK
Christoph
Rijksuniversiteit Groningen
HARRIS
Mark
Curtin University of Technology
HASANOV
Mubariz
Hacettepe University
HAUSMAN
Jerry
MIT
HAYAKAWA
Kazuhiko
Hiroshima University
HENDRICKS
Nathan
Kansas State University
HESHMATI
Almas
Korea University
HODERLEIN
Stefan
Boston College
HOLLY
Sean
Cambridge University
HONORE
Bo
Princeton University
HORNY
Guillaume
Banque de France
HSU
Chih-Chiang
National Central University
HUIBAN
Jean-Pierre
INRA ALISS and UPEC ERUDITE
ISOGAWA
Daiya
University of Tokyo
ISSLER
Joao Victor
Getulio Vargas Foundation
JAILLET
Pierre
Banque de France
JAVDANI
Mohsen
Simon Fraser University
JOCHMANS
Koen
Sciences Po
KAMIONKA
Thierry
CREST – ENSAE - CNRS
KAO
Chihwa
Syracuse University
KARABIYIK
Hande
Maastricht University
KARAMAN ÖRSAL
Deniz Dilan
Leuphana Univ. Lüneburg (Center for Methods)
KARIMI
Mohammad
University of Ottawa
KESINA
Michaela
ETH Zurich
KHALAF
Lynda
Carleton University
KICHIAN
Maral
Bank of Canada
KITAZAWA
Yoshitsugu
Kyushu Sangyo University
KOLTAY
Gábor
European Commission
KONYA
Laszlo
La Trobe University
KREMP
Elisabeth
Banque de France
KRISHNAKUMAR
Jaya
University of Geneva
KUMBHAKAR
Subal
SUNY Binghamton-Stavanger Univ. Norway
KWAK
Do Won
The University of Queensland
LAGET
Edith
IMF
LAMARCHE
Carlos
University of Oklahoma
LE BIHAN
Herve
Banque de France
LE GOFF
Maelan
CEPII
LECAT
Rémy
Banque de France
LIZAL
Lubomir
CERGE-EI
MAASOUMI
Esfandiar
Emory University
MADSEN
Edith
Technical University of Denmark
MAGAZZINI
Laura
University of Verona
MAGNAC
Thierry
University Toulouse 1 Capitole
MAIRESSE
Jacques
University of Maastricht - ENSAE
MAMMI
Irene
University of Bologna
MANRESA
Elena
CEMFI
MASTROMARCO
Camilla
University of Salento
MATHIEU
Claude
Université Paris Est, ERUDITE
MATYAS
Laszlo
CEU
MCLOUGHLIN
Cameron
Banque de France
MESONNIER
Jean-Stéphane
Banque de France
MINEA
Alexandru
University of Auvergne
MOHNEN
Pierre
Maastricht University
MONTMARTIN
Benjamin
Jean Monnet University of Saint-Etienne
MONTORNES
Jérémi
Banque de France
MULKAY
Benoit
Université de Montpellier 1
MURIS
Chris
Simon Fraser University
MUSSIDA
Chiara
Catholic University of the Sacred Heart
NACHBAUR
Yves
Banque de France
NEWEY
Whitney
MIT
NGUYEN-VAN
Phu
University of Strasbourg
OTERO
Jesus
Universidad del Rosario
PACINI
David
University of Bristol
PAKEL
Cavit
University of Oxford
PALM
Franz
Maastricht University
PESARAN
Hashem
Cambridge Univ.-Southern California Univ.
PFISTER
Christian
Banque de France
PIROTTE
Alain
University of Paris II
PLEUS
Milan
University of Amsterdam
POLDERMANS
Rutger
University of Amsterdam
POPESCU
Ruxandra
Banque de France
PREGET
Raphaele
INRA
PUA
Andrew
Universiteit van Amsterdam
PUS
Daria
Central European University
RATFAI
Attila
Central European University
ROCHER
Emmanuel
Banque de France
ROGER
Muriel
INRA - Banque de France
ROLING
Christoph
Bonn Graduate School of Economics
ROSZBACH
Kasper
Sveriges Riksbank - Groningen University
RUYSSEN
Ilse
Ghent University
SARAFIDIS
Vasilis
University of Sidney
SAVIGNAC
Frédérique
Banque de France
SEILER
Christian
Ifo Institute
SERLENGA
Laura
University of Bari
SEVESTRE
Patrick
Banque de France – Université Paris 1
SHAPIRO
Matthew
University of Michigan
SHIN
Yongcheol
University of York
SICKLES
Robin
Rice University
SICSIC
Pierre
Banque de France
SILVA
Andres
University of Kent
SMEEKES
Stephan
Maastricht University
SMITH
L. Vanessa
University of Cambridge
SPANOS
Aris
Virginia Tech
SPARRMAN
Victoria
Statistics Norway
STEVANOVIC
Dalibor
Université du Québec à Montréal
STOPS
Michael
Institute for Employment Research
STROBEL
Thomas
Ifo Institute for Economic Research
SU
Liangjun
Singapore Management University
SUN
Kai
Aston University
SUN
Yiguo
University of Guelph
TAKAYAMA
Noriyuki
RIPPA and Hitotsubashi University
TEPAUT
Marine
Banque de France
TRAPANI
Lorenzo
Cass Business School, London
TROGNON
Alain
GENES
TSANGARIDES
Charalambos
International Monetary Fund
URBAIN
Jean-Pierre
Maastricht University
URGA
Giovanni
Cass Business School
VERDUGO
Gregory
Banque de France
VERMEULEN
Philip
ECB
VICARD
Vincent
Banque de France
VIDANGOS
Ivan
Federal Reserve Board
VIGNERON
Alexandre
Banque de France
VILLETELLE
Jean-Pierre
Banque de France
VOIA
Marcel
Carleton University
VON PETER
Goetz
Bank for International Settlements
WANSBEEK
Tom
University of Groningen
WEIDNER
Martin
University College London
WILSON
Paul
Clemson University
YAMAGATA
Takashi
University of York
YANG
Yu-Wei (Tony)
George Mason University
YANG
Zhenlin
Singapore Management University
YU
Jihai
Peking University
ZHAO
Xueyan
Monash University
ZIELINSKA-GLEBOCKA
Anna
National Bank of Poland