**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**

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