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
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
Regression and Nonlinear Least Squares Estimation
The
Generalized Regression Model
Applications
of Feasible GLS (Two Step) Estimation
Some Linear
Models for Panel Data
Maximum
Likelihood Estimation
Applications
of Maximum Likelihood Estimation and a Two Step Estimator
Generalized
Method of Moments - GMM Estimation
Models for
Discrete Choice
Simulation
Based Estimation - Classical and Bayesian
Sample
Selection
Time Series
Data
Non and
Semiparametric Approaches - Quantile Regression
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course home page.