Professor William Greene’s
Econometric Analysis of Panel Data
Class Notes教學課件PPT檔案下載網址

http://pages.stern.nyu.edu/~wgreene/Econometrics/PanelDataNotes.htm

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. Bayesian Estimation Gibbs Sampling, Markov Chain Monte Carlo, Multinomial Choice, Economics and Marketing Application
Class 24. Modeling Heterogeneity
Class 25. Semiparametric Approaches