B30.3351: 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 and Davidson and MacKinnon's J 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 or Microsoft Word document files.
The
Paradigm of Econometrics
Conditional
Means and the Linear Regression Model
Linear
Least Squares
Least
Squares Algebra - Partial Regression and Partial Correlation
Regression
Algebra and a Fit
Measure; Restricted Least Squares
Finite Sample Properties of the
Least Squares Estimator
Finite Sample Properties of the
Restricted Least Squares Estimator
Estimating the Variance of the
Least Squares Estimator
Hypothesis Testing in the Linear
Regression Model
Hypothesis Tests: Analytics
and an Application
Prediction
in the Classical Regression Model
Data from Christensen and Greene
(JPE, 1976) used in Notes 9
Asymptotic Distribution Theory
Asymptotic Results for the
Classical Regression Model
Instrumental Variables Estimation
Nonlinear Least Squares
Estimation
The Generalized Regression Model
Applications of Feasible GLS
(Two Step) Estimation
Some Linear Models for Panel Data
The Seemingly Unrelated
Regressions Model
Maximum Likelihood Estimation
Aspects of Maximum Likelihood
Estimation
Applications of Maximum
Likelihood Estimation and a Two Step Estimator
GMM Estimation
Models for Discrete Choice
Simulation Based Estimation - Classical and Bayesian
Sample Selection
Time Series Data
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