課程資訊

Econometrics (Ⅱ)

104-2

Fin8037

723 D6100

http://homepage.ntu.edu.tw/~chingihuang/teaching/Metrics2011

Course Outline
1. Review of the Least Squares Theory

2. Nonlinear Least Squares (NLS) Theory
2.1 Nonlinear specifications
2.2 NLS estimator
2.3 Asymptotic properties of the NLS estimator
2.4 Large sample tests

3. Quasi-Maximum Likelihood (QML) Theory
3.1 Kullback-Leibler information criterion
3.2 Asymptotic properties of the QML estimator
3.3 Information matrix equality
3.4 Large sample tests — Nested
3.5 Large sample tests — Non-nested
3.6 Applications: Microeconometric models
3.7 Applications: ARMA models
3.8 Applications: Volatility models

4. Generalized Method of Moment (GMM)
4.1 GMM estimation
4.2 Asymptotic properties of the GMM estimator
4.3 Over-identifying restrictions test
4.4 Other GMM estimators
4.5 Estimation of conditional moment restrictions
4.5 Applications:

This is the second course in econometric theory for Ph.D. students; well prepared Master students are also welcome to take this course. As a prerequisite, students are expected to have completed Econometric Theory I at Ph.D. level or something equivalent. In this course, we will follow my own lecture notes and focus on three leading econometric methods:
nonlinear least squares method, quasi-maximum likelihood method, and generalized method of moment. As in the first course, I will not provide “recipes” for these methods. Instead, I hope students will learn from this course how an econometric method is derived and why it works. All lectures will be in English; students are encouraged to participate classroom discussion which is a crucial part of the Ph.D. training.

Office Hours

1. Davidson, R. and J. G. MacKinnon, Estimation and Inference in Econometrics, New York, NY: Oxford University Press, 1993.
2. Greene, W. H., Econometric Analysis, 6th ed., Upper Saddle River, NJ: Pearson Prentice Hall, 2008.
3. Hamilton, J., Time Series Analysis, Princeton, NJ: Princeton University Press, 1994.

(僅供參考)

 No. 項目 百分比 說明 1. no 100%

 課程進度
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