2015-04-08
Institute for Evaluation of Labour Market and Education Policy (IFAU) and
Uppsala Center for Labor Studies (UCLS), Uppsala University
invite to a course in
Advanced Microeconometrics
June 15–18, 2015 at Uppsala University, Sweden
The course is lectured by A. Colin Cameron, University of California – Davis
Goal
This course presents several special topics in microeconometrics.
The course will illustrate the various methods using Stata, and Stata programs
and datasets will be provided. A complete set of overheads will be provided.
Presumed background
Nonlinear methods: Maximum likelihood estimator, nonlinear least squares
estimator, asymptotic theory for m-estimators, statistical inference, gradient
methods, computation of marginal effects, nonlinear GMM.
The number of participants is restricted to 40.
If you have any questions, please contact Per Johansson
per.johansson@statistik.uu.se
Please register to Anette Olsson: anette.olsson@ifau.uu.se no later than May
29.
Welcome!
Per Johansson and Per-Anders Edin
2015-04-08
Organization
The daily schedule is:
09:30 - 11:00: First lecture
11:00 - 11:30: Break
11:30 - 13.00: Second lecture
13:00 - 14:00: Lunch
14:00 - 15:30 Computer lab
Course outline
Day 1: Count Regression
Lecture 1: Basic cross-section methods: Poisson, negative binomial, hurdle, zero-inated.
Lecture 2: More advanced methods: mixtures, endogeneity, panel data.
Computer Lab: Some general Stata and Stata for Counts
Day 2: Inference for Clustered Data
Lecture 1: Clustered Data: OLS with cluster-robust standard errors, feasible GLS,
serially correlated errors, random effects, mixes models; bootstrap without asymptotic
refinement.
Lecture 2: Clustered Data: Fixed effects; what to cluster over; twoway clustering;
spatial correlation.
Computer Lab: Stata for Clustered Data
Day 3: End Inference for Clustered Data and Begin Simulation Methods
Lecture 1: Clustered Data: Few clusters; bootstrap with asymptotic refinement;
nonlinear models, endogenous regressors.
Lecture 2: Simulation: Pseudo random draws, Monte Carlo integration, Gaussian quadrature,
Monte Carlo experiment.
Computer Lab: Stata for bootstrap and Monte Carlo experiments.
Day 4: End Simulation Methods
Lecture 1: Maximum simulated likelihood, Bayesian approach, Bayesian analytical
example, Bayesian example in Stata 14 (I do not assume you have Stata 14)
Lecture 2: Nonparametric and Semiparametric Estimation.
Computer Lab: Stata for MSL and for non and semi-parametric regression.
2015-04-08
Course material
The main material is overhead slides that will be provided and that are self-contained.
Stata programs and data sets will be posted at the course website. My programs are for
Stata 12 but should also run in Stata 10, 11, and 13.
The main references will be:
A.C. Cameron and P.K. Trivedi (2005), Microeconometrics: Methods and Applications,
Cambridge University Press.
A.C. Cameron and P.K. Trivedi (2005) Microeconometrics using Stata.
Plus some relevant papers including A.C. Cameron and D.L. Miller (2015) “A
Practitioner's Guide to Cluster-Robust Inference”.
Available at http://cameron.econ.ucdavis.edu/research/Cameron Miller JHR 2015
February.pdf