Econometric Analysis
of Panel Data
Class Notes
Professor William Greene
Department of Economics
Office:MEC 7-90, Ph. 998-0876
e-mail:wgreene@stern.nyu.edu
URL: http://people.stern.nyu.edu/wgreene
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 .pptx files.
1.
Introduction to Econometrics; Introduction to the course
2.
Statistical Models: Estimation and Testing; The linear model
2-A. Endogeneity in the linear model
3.
Models with Individual Effects
4.
Fixed Effects and Hierarchical Models
4-A. Minimum Distance Estimation
5.
Random Effects Models
6.
Random Effects Model: Maximum Likelihood Estimation. Panel Data Structures
7.
Extensions of Effects Models; Time Varying Fixed Effects, Heteroscedasticity,
Measurement Error, Spatial Autocorrelation
8.
Instrumental Variables; The Hausman-Taylor Estimator, GMM Estimation
9.
GMM Estimation, Dynamic Models, Arellano/Bond/Bover, Schmidt and Ahn
10.
Dynamic Models, Time Series, Panels and Nonstationary Data
11.
Heterogeneous Parameter Models (Fixed and Random Effects), Two Step Analysis of
Panel Data Models
12. Random Parameters, Discrete Random Parameter
Variation, Continuous Parameter Variation
13. Linear Regression and
Nonlinear Modeling
14.
Nonlinear Models and Nonlinear Optimization; ML Estimation, M Estimation, GMM
Estimation
15.
Classical Estimation of Nonlinear Effects Models; Random and Fixed Effects
Binary Choice Models
16.
Random and Fixed Effects in Nonlinear Models, Quadrature and Simulation
17.
Discrete Choice Models for Spatial Data
18.
Ordered Choices and Censored Dependent Variables - Microeconometrics
19.
Limited Dependent Variable Models and Models for Count Data
20.
Sample Sample Selection Models and Models of Attrition
21.
Hazard Function and Duration Models
22.
Stochastic Frontiers and Efficiency Estimation, Applications from the
Stochastic Frontiers Literature
23.
Random Parameters Models, Heterogeneity, Second Generation, Simulation Based
Estimation
24.
Multinomial Choice and Stated Choice Experiments
24-A. Multinomial Choice Model Extensions:
Best/Worst Data, Hybrid Choice
25.
Bayesian Estimation Gibbs Sampling, Markov Chain Monte Carlo, Multinomial
Choice, Economics and Marketing Application
26.
Bayesian Methods and Models of Heterogeneity
FINAL
EXAM (NO CLASS MEETING)
Click here to download the final examination.