ECO 746
Advanced Econometrics II
UNC Greensboro
Fall 2014
Contact Information
Name:
Office:
Email:
Chris Swann
4?? Bryan
chris_swann@uncg.edu
Course Overview
ECO 746 is the second Econometrics course in the PhD program. Its purpose is to move from the
linear model to an analysis of a number of non-linear models and to consider a number of other
issues in microeconometric analysis. In addition to understanding the mechanics of the
econometric models, we will also discuss the application of the methods.
Office Hours
Open door policy – i.e., if I am in the office and not in the middle of something I will meet with
you – and by appointment. Email is the best way to contact me to make an appointment.
Learning Objectives
1) Understand different estimation tools including maximum likelihood, simulation, survey
sampling, and bootstrapping.
2) Learn the basics of non-linear optimization techniques.
3) Have a firm understanding of an array of econometric models including discrete choice
models, selection bias models, count data models, panel data and duration models. This
understanding should include the theoretical underpinnings, estimation, and
interpretation.
4) Develop a portfolio of Stata code that you can return to in the future as you encounter new
problems.
Texts
In general, read Cameron and Trivedi (2005) first and supplement with Wooldridge and/or
Greene. Cameron and Trivendi (2010) will be used for labs but may be helpful for lectures as
well.
Cameron, C and P. Trivedi. 2005. Microeconometrics: Methods and Applications. Cambridge
University Press. [Referred to as “CT” in the calendar below.]
Cameron, C and P. Trivedi. 2010. Microeconometrics Using Stata, Revised Ed. Stata Press.
[Referred to “CTS” in the calendar below.]
Greene, William H. 2012. Econometric Analysis, Seventh Edition Prentice Hall. [Referred to as
“G” in the calendar below.]
Wooldridge, Jeffrey M. 2010. Econometric Analysis of Cross Section and Panel Data, 2nd ed.
MIT Press. [Referred to as “W” in the calendar below.]
Grading
Grades will be based on homework assignments (40%), a midterm (30%), and a final (30%).
Homework assignment dates will be announced.
Tentative Schedule
Date
Aug 19
Topic
Maximum Likelihood
Aug 21
Numerical Optimization
Aug 26
Aug 28
Lab
Simulation Based Estimation
Sept 2
Sept 4
Bootstrapping standard errors
Survey Sampling and Weights
Sept 9
Sept 11
Lab
Binary Discrete Choice Models
Sept 16
Unordered Choice Models
Sept 18
Sept 23
Ordered Choice Models
Count Data Models
Sept 25
Lab
Sept 30
Endogeneity in Non-linear Models
Oct 2
Oct 7
Oct 9
Oct 16
Endogeneity in Non-linear Models
Corner Solutions and Censored Outcomes
Midterm
Sample Selection and Two-Part Models
Oct 21
Oct 23
Oct 28
Oct 30
Nov 4
Nov 6
Nov 11
Nov 13
Treatment Effects
Treatment Effects
Lab
Survival Analysis
Survival Analysis
No Class
Mixture Models and Unobserved Heterogeneity
Non-Linear Panel Models
Nov 18
Nov 24
Non-Linear Panel Models
Dynamic Panel Models
TBA
Final
Reading (Chapters)
CT: 5
W: 13.1 to 13.7
G: 14.1-14.6
CT: 10
W: 12.7
G: Appendix E
CTS: 10, 11.1-11.7
CT: 12; W: 12.8.1
G: 15.1-15.2, 15.6
CT: 11; W: 12.8.2; G: 15.4
CT: 24.1-24.5, 24.8-24.9
W: 20
CTS: 4, 5.5, & 13
CT: 14; W: 15.1-15.7
G: 17.1-17.3.4
CT: 15.1-15.8, 15.12-15.13
W: 16.2; G: 17.5, 18.2-18.3
CT: 15.9; W: 16.3
CT: 20; W: 18.1-18.4
G: 18.4.1-18.4.6
CTS: 14.1-14.7, 15.1-15.4,
15.7, 15.9, 17.1-17.3.4
W: 15.7.2-15.7.3, 16.2.3,
16.3.3, 17.5.3, 18.5
G: 17.3.5, 18.4.9
Assignment
CT: 17.1-17.4
Midterm
CT: 16, 19.1-19.9
G: 18.4.8, 19.5.1-19.5.4
CT: 15; W: 21; G: 19.6
CTS: 14.8, 16, 17.5
CT: 17; W: 20; G: 19.4
CT: 18
CT: 23
W: 15.8, 16.8, 17.7, 19.6
G: 11.9, 17.4, 18.4.7, 19.5.5
CT: 22.6; W: 11.1-11.6
G: 11.8
Final
Tentative List of Additional Readings by Topic (not required though *’ed readings are
encouraged):
I. Numerical Optimization
McCullough, B.D. and Charles G. Renfro, “Some numerical aspects of nonlinear estimation,”
Journal of Economic and Social Measurement, 26 (2000), 63-77.
Quandt, Richard E., “Computational Problems and Methods,” Handbook of Econometrics,
Volume I, 1983. (Available on-line – do a Google search.)
II. GMM
Wooldridge, Jeffrey M., “Applications of Generalized Method of Moments Estimation,” The
Journal of Economic Perspectives, 15:4, Fall 2001.
Hall, Alastair, “An Introduction to Generalized Method of Moments Estimation,” unpublished
working paper, November 1991.
III. Simulation
*Stern, Steven, “Simulation-Based Estimation,” Journal of Economic Literature, 35:4,
December 1997, 2006-2060.
IV. Discrete Choice Models
Amemiya, Takeshi, “Qualitative Response Models: A Survey,” Journal of Economic Literature,
19 (December 1981), pp 1483-1536.
Pagan, Adrian and Frank Vella, “Diagnostic Tests for Models Based on Individual Data: A
Survey,” Journal of Applied Econometrics, Vol 4 (supplement), (Dec 1989), S29-S59.
Horowitz, Joel and N. E. Savin, “Binary Response Models: Logits, Probits, and
Semiparametrics,” Journal of Economic Perspectives, 15(4), Fall 2001, pp 43-56.
Pregibon, Daryl, “Logistic Regression Diagnostics,” The Annals of Statistics, 9:4 (1981), 705724.
Landwehr, James M., Daryl Pregibon, and Anne C. Shoemaker, “Graphical Methods for
Assessing Logistic Regression Models,” JASA, 79:384 (March 1984), 61-71.
Buchmueller, Thomas C. and Paul J. Feldstein, “The Effect of Price on Switching among Health
Plans,” Journal of Health Economics, 16 (1997), 231-247.
Chaloupka, Frank J. and Henry Wechsler, “Price Tobacco Control Policies, and Smoking among
Young Adults, Journal of Health Economics, 16 (997), 359-373.
Nested Logit
Feldman, Roger, Michael Finch, Bryan Dowd, and Steven Cassou, “The Demand for
Employment-Based Health Insurance Plans,” The Journal of Human Resources, 24:1
(Winter, 1989), 115-142.
Hensher, David A. and William H. Greene, “Specification and Estimation of the Nested Logit
Model: Alternative Normalizations,” Transportation Research, Part B, 36 (2002), 1-17.
Koppelman, Frank S. and Chieh-Hua Wen, “Alternative Nested Logit Models: Structure,
Properties, and Estimation,” Transportation Research, Part B, 32:5 (1998), 289-298.
Heiss, Florian, “Specification(s) of Nested Logit Models,” unpublished manuscript, January
2002.
Multinomial and Conditional Logit
McFadden, Daniel, “Modelling the Choice of Residential Location,” in Spatial Interation Theory
and Planning Models (Karlqvist, Lundqvist, Snickars, and Weibull, eds), North Holland
Publishing Company, 1978.
Hausman, Jerry and Daniel McFadden, “Specification Tests for the Multinomial Logit Model,”
Econometrica, 52:5 (September 1984), 1219-1240.
McFadden, Daniel, Kenneth Train, and William B. Tye, “An Application of Diagnostic Tests for
the Independence From Irrelevant Alternatives Property of the Multinomial Logit
Model,” publication information???.
Small, Kenneth A. and Cheng Hsiao, “Multinomial Logit Specification Tests,” International
Economic Review, 26:3 (October 1985), 619-627.
V. Corner Solutions and Censored Outcomes
Amemiya, Takeshi, “Tobit Models: A Survey,” Journal of Econometrics, 24 (January-February
1984), pp 3-61.
Greene, William, “Marginal Effects in the Censored Regression Model,” Economics Letters, 64
(1999), 43-39.
VI. Selection Bias
Heckman, James J., “The Common Structure of Statistical Models of Truncation, Sample
Selection, and Limited Dependent Varialbes, and a Simple Estimator for Such Models,”
Annals of Economic and Social Measurement, 5:4, 1976, pp 475-492.
*Heckman, James J., “Sample Selection Bias as a Specification Error,” Econometrica, 47(1),
January 1979, pp 153-161.
Berk, Richard A., “An Introduction to Sample Selection Bias in Sociological Data,” American
Sociological Review, 48 (June 1983), pp 386-398.
Heckman, James, “Varieties of Selection Bias,” American Economic Review, May 1990, 313318.
Vella, Francis, “Estimating Models with Sample Selection Bias,” The Journal of Human
Resources, 33:1, Winter 1998.
Puhani, Patrick A., “The Heckman Correction for Sample Selection and its Critique,” Journal of
Economic Surveys, 14:1 (2000), pp 53-68.
Lee, Lung-Fei, “Tests for the Bivariate Normal Distribution in Econometric Models with
Selectivity, Econometrica, 52:4 (July 1984), 843-863.
Schmertmann, Carl P., “Selectivity Bias Correction Methods in Polychotomous Sample
Selection Models,” Journal of Econometrics, 60 (1994), 101-132.
VII. Two Part Models and Comarisons to Selection Bias Models
Duan, Naihua, Willard G. Manning, Jr., Carl N. Morris, and Joseph P. Newhouse, “A
Comparison of Alternative Models for the Demand for Medical Care,” Journal of
Business and Economic Statistics, 1:2 (April 1983), 115-126.
Hay, Joel W. and Randell J. Olsen, “Let them Eat Cake: A Note on Comparing Alternative
Models of the Demand for Medical Care,” Journal of Business and Economic Statistics,
2:3 (July 1984), 279-282.
Duan, Naihua, Willard G. Manning, Jr., Carl N. Morris, and Joseph P. Newhouse, “Choosing
Between the Sample-Selection Model and the Multi-Part Model,” Journal of Business
and Economic Statistics, 2:3 (July 1984), 283-289.
Hay, Joel W., Robert Leu, and Paul Rohrer, “Ordinary Least Squares and Sample Selection
Models of Health-Care Demand: A Monte Carlo Comparison,” Journal of Business and
Economic Statistics, (October 1987), 499-506.
Manning, W.G., N. Duan, and W.H. Rogers, “Monte Carlo Evidence on the Choice Between
Sample Selection and Two Part Models,” Journal of Econometrics, 35 (1987), 59-82.
Leung, Siu Fai and Shihti Yu, “On the Choice Between Sample Selection and Two-Part
Models,” Journal of Econometrics, 72 (1996), 197-229.
Transformation and Re-transformation
Mullahy, John, “Much Ado about Two: Reconsidering Retransformation and the Two-Part
Model in Health Econometrics,” Journal of Health Economics, 17 (1998), pp 247-281.
Manning, Willard G., “The Logged Dependent Variable, Heteroscedasticity, and the
Retransformation Problem,” Journal of Health Economics, 17 (1998), pp 283-295.
Ai, Chunrong and Edward C. Norton, “Standard Errors for the Retransformation Problem with
Heteroscedasticity,” Journal of Health Economics, 19 (2000), pp 697-718.
Manning, Willard G. and John Mullahy, “Estimating Log Models: To Transform or not to
Transform?,” Journal of Health Economics, 20 (2001), pp 461-494.
VIII. Count Data Models
TBA
IX. Survival Analysis
* Kiefer, Nicholas M., “Economic Duration Data and Hazard Functions,” Journal of Economic
Literature, 26 (June 1988), pp. 646-679.
Blank, Rebecca, “Analyzing the Length of Welfare Spells,” Journal of Public Economics, 39,
(1989), pp 245-273.