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

**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**

** **