Econometrics I

Class Notes

Professor W. 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 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 (Additional notes on 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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