ECON-GB30.3351.01: Econometrics I
Class Notes
Professor W. Greene
Department of Economics
Office:MEC 7-78, Ph. 998-0876, Fax. 995-4218
e-mail:wgreene@stern.nyu.edu
WWW: http://stern.nyu.edu/~wgreene
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Abstract: This is an intermediate level, Ph.D. course in Applied Econometrics. Topics to be studied include specification, estimation, and inference in the context of models that include then extend beyond the standard linear multiple regression framework. After a review of the linear model, we will develop the asymptotic distribution theory necessary for analysis of generalized linear and nonlinear models. We will then turn to instrumental variables, maximum likelihood, generalized method of moments (GMM), and two step estimation methods. Inference techniques used in the linear regression framework such as t and F tests will be extended to include Wald, Lagrange multiplier and likelihood ratio and tests for nonnested hypotheses such as the Hausman specification test. Specific modelling frameworks will include the linear regression model and extensions to models for panel data, multiple equation models, and models for discrete choice.
Notes: The following list points to the class discussion notes for Econometrics I. These are Power Point Presentation files.
1. The Paradigm
of Econometrics
2. Conditional
Means and the Linear Regression Model
3. Linear Least
Squares
4. Least Squares
Algebra - Partial Regression and Partial Correlation
5. Regression
Algebra and a Fit Measure; Restricted Least Squares
6. Finite Sample
Properties of the Least Squares Estimator
7. Estimating
the Variance of the Least Squares Estimator
8. Hypothesis
Testing in the Linear Regression Model
9. Hypothesis
Tests: Analytics and an Application
10. Prediction in
the Classical Regression Model
11. Asymptotic
Distribution Theory
12. Asymptotic
Results for the Classical Regression Model
13. Instrumental
Variables Estimation
14. The
Generalized Regression Model
15. Applications
of Feasible GLS (Two Step) Estimation
16. Some Linear
Models for Panel Data
17. Nonlinear Regression Models
18. Maximum
Likelihood Estimation
19. Applications
of Maximum Likelihood Estimation and a Two Step Estimator
20. Sample
Selection
21. Generalized
Method of Moments - GMM Estimation
22. Non- and Semiparametric Approaches - Quantile Regression
23. Simulation Based Estimation
24. Bayesian Analysis
25. Time Series
Data
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