Econometrics I

Class Notes

Professor W. Greene
Department of Economics
Office: MEC 7-90, Ph. 998-0876
e-mail: wgreene@stern.nyu.edu
Home page: http://people.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. 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 (.pptx) files and pdf documents (.pdf).

1. Introduction: Paradigm of Econometrics (pptx) (pdf)


2. The Linear Regression Model: Regression and Projection (pptx) (pdf)


3. Linear Least Squares, Regression Fit, Transformations (pptx) (pdf)


4. Frisch-Waugh Theorem, Least Squares, Partial Regression and Partial Correlation (pptx) (pdf)


5. Finite Sample Properties of Least Squares, Multicollinearity (pptx) (pdf)


6. Robust Covariance Matrix Estimation, Clustering, Bootstrap (pptx) (pdf)


7. Nonlinearities, Interactions, Prediction, Oaxaca Decomposition (pptx) (pdf)


8. Interval Estimation, Hypothesis Testing, Restrictions (pptx) (pdf)


9. Hypothesis Tests: Structural Break, Specification, Non-nested Models (pptx) (pdf)


10. Asymptotic Distribution, Delta Method, Krinsky and Robb, Nonlinear Partial Effects (pptx) (pdf) (Additional notes on asymptotic distribution theory)


11. Asymptotic Distribution Theory for Linear Regression, Wald Tests, Robust Inference (pptx) (pdf)


12. Endogeneity, Instrumental Variables, Two Stage Least Squares, Treatment Effects (pptx) (pdf) (John Snow invented IV - Notes)


13. Intermission, Review (pptx) (pdf)


14. The Generalized Regression Model (pptx) (pdf)


15. Applications of Feasible GLS (Two Step) Estimation (pptx) (pdf)


16. Linear Models for Panel Data (pptx) (pdf)


17. Nonlinear Regression Models (pptx) (pdf)


18. Maximum Likelihood Estimation (pptx) (pdf)


19. Applications of Maximum Likelihood Estimation and a Two Step Estimator (pptx) (pdf)


20. Sample Selection (pptx) (pdf)


21. Generalized Method of Moments - GMM Estimation (pptx) (pdf)


22. Non- and Semiparametric Approaches - Quantile Regression (pptx) (pdf)


23. Simulation Based Estimation (pptx) (pdf)


24. Bayesian Analysis (pptx) (pdf)


25. Time Series Data (pptx) (pdf)

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