Stern School of Business

B55.9912: Econometric Analysis of Panel Data

Class Notes

Professor William Greene
Department of Economics
Office:MEC 7-78, Ph. 998-0876, Fax. 995-4218

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Abstract: This is an intermediate level, Ph.D. course in the area of  Applied Econometrics dealing with Panel Data.  The range of topics covered in the course will span a large part of econometrics generally, though we are particularly interested in those techniques as they are adapted to the analysis of 'panel' or 'longitudinal' data sets.  Topics to be studied include specification, estimation, and inference in the context of models that include individual (firm, person, etc.) effects.  We will  begin with a development of the standard linear regression model, then extend it to panel data settings involving 'fixed' and 'random' effects.  The asymptotic distribution theory necessary for analysis of generalized linear and nonlinear models will be reviewed or developed as we proceed.. We will then turn to instrumental variables, maximum likelihood, generalized method of moments (GMM), and two step estimation methods.  The linear model will be extended to dynamic models and recently developed GMM and instrumental variables techniques.   The classical methods of maximum likelihood and GMM and Bayesian methods, expecially MCMC techniques, are applied to models with individual effects.  The last third of the course will focus on nonlinear models.  Theoretical developments will focus on heterogeneity in models including random parameter variation, latent class (finite mixture) and 'mixed' and hierarchical models.  We will also visit the theory for  techniques for optimization in the setting of nonlinear models.  We will consider numerous applications from the literature, including static and dynamic regression models, heterogeneous parameters models (e.g., Fama-Macbeth), random parameter variation, and specific nonlinear models such as binary and multinomial choice and models for count data. 

Notes: The following list points to the class discussion notes for Econometric Analysis of Panel Data. These are Powerpoint .ppt files.  Individual sets of notes may correspond to more or less than a full day of class.

Class 1. Introduction to Econometrics; Introduction to the course

Class 2. Statistical Models: Estimation and Testing; The linear model

Class 3. Models with Individual Effects

Class 4. Fixed Effects and Hierarchical Models

Class 4-A. Minimum Distance Estimation

Class 5. Random Effects Models

Class 6. Random Effects Model: Maximum Likelihood Estimation. Panel Data Structures

Class 7. Extensions of Effects Models; Heteroscedasticity, Measurement Error, Spatial Autocorrelation,...

Class 8. Instrumental Variables; The Hausman-Taylor Estimator, GMM Estimation

Class 9. GMM Estimation, Dynamic Models, Arellano/Bond/Bover, Schmidt and Ahn

Class 10. Dynamic Models, Time Series, Panels and Nonstationary Data

Class 11. Heterogeneous Parameter Models (Fixed and Random Effects), Two Step Analysis of Panel Data Models

Class 12. Random Parameters, Discrete Random Parameter Variation, Continuous Parameter Variation

Class 13. MIDTERM

Class 14. Nonlinear Models and Nonlinear Optimization; ML Estimation, M Estimation, GMM Estimation

Class 15. Classical Estimation of Nonlinear Effects Models; Random and Fixed Effects Binary Choice Models

Class 16. Random and Fixed Effects in Nonlinear Models, Quadrature and Simulation

Class 17. Dynamic Discrete Choice Models and Incidental Parameters Problems

Class 18. Ordered Choices and Censored Dependent Variables - Microeconometrics

Class 19. Limited Dependent Variable Models and Models for Count Data

Class 20. Sample Sample Selection Models and Models of Attrition

Class 20-A. Hazard Function and Duration Models

Class 21. Stochastic Frontiers and Efficiency Estimation, Applications from the Stochastic Frontiers Literature

Class 22. Random Parameters Models, Heterogeneity, Second Generation, Simulation Based Estimation

Class 23. Multinomial Choice and Stated Choice Experiments

Class 24. Bayesian Estimation Gibbs Sampling, Markov Chain Monte Carlo, Multinomial Choice, Economics and Marketing Application

Class 25. Bayesian Methods and Models of Heterogeneity


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