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Econometrics 671 ... intended to review procedures for estimating systems of equations (focusing on ... W. Greene's Econometric Analysis, 5th edition, 2003.

SYLLABUS Econometrics 671 Fall 2004 Instructor: Justin L. Tobias Office: 367 Heady Phone: 294-5028 Office Hours: Mon/Wed 1:00-2:30 email:[email protected] Class Web Site: 1. Course Description This is the third(!) portion of the first semester Ph.D. econometrics course. This course is primarily intended to review procedures for estimating systems of equations (focusing on SUR, panel and simultaneous systems). In the last few lectures of the course we will also review nonparametric density and regression estimation as well as topics in Bayesian econometrics. Throughout the course, we will apply techniques discussed in the classroom using MATLAB, which is available at the computer labs. MATLAB is very similar to Gauss, which I know you have used before. I will teach you how to write your own m-files and help you to do the problems on the problem sets using MATLAB. The programming side of the course should not be excessively demanding. 2. Grading and Textbooks The grade from my portion of the class will be divided (50-50) among problem sets and a final exam. Final grades from the courses will then be based on your overall scores from the three portions of the class. The required textbook is Econometric Analysis of Cross Section and Panel Data by Wooldridge. Though we will follow this book, you are only responsible for the topics covered in the lectures. Some other books that may prove to be of value are: W. Greene’s Econometric Analysis, 5th edition, 2003. (A good general purpose reference)


Some References on Nonparametrics

Blundell, R. and A. Duncan (1998), “Kernel Regression in Empirical Microeconomics” Journal of Human Resources 33: 62-87. Dinardo, J. Fortin, N. and Lemieux, T. (1996). “Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach” Econometrica DiNardo, J. and Tobias, J. (2001), “Nonparametric Density and Regression Estimation” Journal of Economic Perspectives, 15(4), 11-28. (Can download from my website). Fan, J. and I. Gijbels (1996). Local Polynomial Modeling and its Applications. Chapman & Hall. Hardle, W. (1990). Applied Nonparametric Regression, Cambridge University Press. Silverman, B. (1986). Density Estimation for Statistics and Data Analysis (New York: John Wiley & Sons, Inc.). Yatchew, A. (1997). 57(2):135–143.

“An Elementary Estimator of the Partial Linear Model.”

Economics Letters,

Yatchew, A. (1998). “Nonparametric Regression Techniques in Economics. ” Journal of Economic Literature 36, 669-721.


Some References on Bayesian Econometrics

Carlin, B. and Louis, T. Bayes and Empirical Bayes Methods for Data Analysis Chapman and Hall 2nd ed, 2000. Gelman, A., J.B. Carlin, H.S. Stern and D.B. Rubin Bayesian Data Analysis Chapman and Hall 2nd ed, 2004. Koop, G. Bayesian Econometrics Wiley, 2003. Lancaster, T. An Introduction to Modern Bayesian Econometrics Blackwell, 2004. Poirier, D.J. Intermediate Statistics and Econometrics, 1995. Poirier, D.J. and J.L. Tobias Bayesian Econometrics. Can get from my website. 4. Course Outline The following is a very rough outline of the topics covered in this course. I have broken them down into topics I expect we will cover, although we may move faster or slower than expected. (2 Weeks): Systems of Equations: SUR, Panel, SEMs. Identification and Estimation via ML and Two Step Methods. (1.5 Weeks): Nonparametric Density and Regression Estimation: Estimation, Pointwise Consistency, Partially Linear Models. (1.5 Weeks): Bayesian Basics: A Quick overview and comparison with Classical Methods, Point and Interval Estimation, Prediction, Testing, Examples with Conjugate Priors, the Linear Regression Model.