Professor Peter C. Reiss
Spring 2004
Time and location TBA
Econometric Methods – III
Administrative Details
First Meeting: Friday April, 2 2004, 9:00am, Littlefield 104
Office: Littlefield 215
Office hours: By appointment
Phone: (650) 725-2759
Email: reiss_peter@gsb.stanford.edu
Course Page: http://www.stanford.edu/~preiss/courses.htm
Course Overview
This course completes the GSB’s first-year PhD sequence in econometrics. It covers nonlinear econometric models and estimation methods. The goal is to put students in a position to analyze or produce sophisticated and original applied econometric research.
We begin with a general overview of the properties of extremum estimators. Extremum estimators include many popular nonlinear econometric estimators, including nonlinear least squares (NLS), maximum likelihood (ML), quasi-maximum likelihood (QML), empirical likelihood (EL), and generalized method of moments (GMM) estimators. Our discussions will include rigorous treatments of the large sample behavior of these estimators and testing issues.
The instructor will make every effort to discuss and show how these methods are used in real applications. We also will spend considerable time understanding computational and other practical implementation issues. Most examples will be programmed in MATLAB.
Time permitting, we will briefly consider more advanced topics and applications, including: time series methods, non-parametric estimators and simulation estimators.
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Assessments
Homework: 30 %
Presentation: 10 %
Final Exam: 60 %
There will be 5-7 homework assignments that will account for 30 percent of your grade. Homeworks should be neatly written and clear.
You (and perhaps a classmate) will be assigned a topic during the quarter on which you will prepare and present a 30-minute information session to your classmates. You will also turn in a 3-5 page discussion of your conclusions.
Approximately 60 percent of your grade will come from your performance on the final. (I say approximately, because on the margin, being alert and contributing in class can move borderline cases up a grade (but not down a grade). The final exam will be a 3-hour, in-class and open book examination. The registrar will set the date and time approximately mid-quarter.
There is no official textbook. Since you already have Greene, I will draw some readings from Greene.
Econometric Analysis, 4th Edition, William H. Greene, 2000
Some other general econometrics texts:
Introductory Econometrics: A Modern Approach. Jeffrey M. Wooldridge, 2000.
Econometric Theory, J. Davidson, Basil Blackwell (2000).
Advanced Econometrics, T. Amemiya, Blackwell (1985).
Estimation and Inference in Econometrics, R. Davidson and J.G. MacKinnon, Oxford University Press (1993).
Econometrics, F. Hayashi, Princeton University Press (2000).
An Introduction to Classical Econometric Theory, P.A. Ruud OUP (2000).
In general, you should not rely exclusively on Greene. I encourage you to explore the above options and to seek out alternative texts and articles, especially when working on problems.
Course Outline
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Optional, but recommended
TBA To be arranged.
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SESSIONS 1-2: Overview of Nonlinear Models, Extremum Estimators, Limit Theorems
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Newey and McFadden, Large Sample Estimation and Hypothesis Testing, pp. 2113-2123.
http://www1.elsevier.com/hes/books/02/04/036/0204036.htm
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Greene, 16.1, 16.5, Chapter 9. Review Appendix D.
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Amemiya, Advanced Econometrics, pp. 105-114 (Review Ch 3.)
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Davidson and MacKinnon, Chapters 2 and 4.
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Wooldridge, “Estimation and Inference for Dependent Processes”, Handbook of Econometrics, Vol. 5, ch. 45.
http://www.elsevier.nl/hes/books/02/04/045/0204045.htm
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SESSIONS 3-6: Nonlinear Least Squares (NLS) and GMM Estimators
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Greene, Chapter 18.
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Hansen, Lars Peter. "Large Sample Properties of Generalized Method of Moments Estimators" Econometrica 50, no.4 (July 1982): 1029-54.
Available from JSTOR http://www.jstor.org/browse#Economics
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Hansen, Lars Peter; Singleton, Kenneth J. "Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models" Econometrica 50, no.5 (September 1982): 1269-86.
Available from JSTOR http://www.jstor.org/browse#Economics
http://www.business.uab.edu/kjang/book/book.htm
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Creel, E-Text, Chapters 18 and 19.
http://econ045.uab.es/omega/Project_001/index.html
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Davidson and MacKinnon, Chapter 5.
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Ruud, Chapters 21-22.
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Matyas, Generalized Method of Moments Estimation, Chs 1 and 2.
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SESSIONS 7-9: Computing Extremum Estimators
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Greene, Appendix E.6.
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Quandt, “Computational Problems and Methods”, pp. 707-723; 728-729.
http://www1.elsevier.com/hes/books/02/01/012/c0201012.htm
(Click on red “Full Text”)
http://econ045.uab.es/omega/Project_001/index.html
(Click on notes.pdf)
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Mike Cliff’s GMM Program Documentation
www.mgmt.purdue.edu/faculty/mcliff/progs/gmmdoc.pdf
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http://www.spatial-econometrics.com/
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Ruud, Chapter 16.
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Matlab Links
http://www4.ncsu.edu/~pfackler/compecon/links.htm
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SESSIONS 10-12: Maximum Likelihood Estimators (EM Algorithm)
SESSIONS 13-15: Inference in Nonlinear Models
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Greene, pp. 175-180, 494-496, and 548-551.
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Engle, Wald, Likelihood Ratio, … Handbook of Econometrics, vol II.
http://www1.elsevier.com/hes/books/02/02/013/0202013.pdf
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Davidson and MacKinnon, pp. 168-174, 435-478.
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Hayashi, 7.3 and 7.4.
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SESSIONS 16-17: Introduction to Nonparametric Methods (Kernels, Nonparametric Regression)
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Greene, 16.3-16.4.
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Hardle and Linton, “Applied Nonparametric Methods” Handbook of Econometrics Volume 4.
http://www1.elsevier.com/hes/books/02/04/038/0204038.pdf
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http://home.tiscalinet.ch/paulsoderlind/Courses/ EmpAssetPhD/EFNonParam.pdf
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SESSIONS 18/19: Introduction to Simulation Estimators
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Greene, Remainder of Appendix E.
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