Type of Credit: Elective
Credit(s)
Number of Students
This course is designed for the first-year graduate students. The aim of the course is to develop familiarity with a wide range of statistical and econometric techniques that have proved to be useful in applied contexts. It covers some topics already covered in Econometrics I, but at a more theoretical level. Theoretical results will be developed as necessary and in order to allow students to apply general principles to their own research problems. Asymptotic theory, non-linear models, panel data model, GMM estimation, discrete choice model, bootstrap methods, and nonparametric regression are among the topics covered in this course.
能力項目說明
The primary emphasis of this course is placed upon applicability, on the ability to understand the statistical and econometric techniques use in the literature, and on acquiring a minimal acquaintance with econometric computing. The material discussed is a reasonable definition of the minimum that a well-trained graduate student should know. For those of you who are primarily interested in econometric theory, the course should give you some idea of the way in which economists attempt to confront theory and evidence.
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
The course will cover the following topics:
1. Review of Linear Regression Model
2. Review of Matrix Algrbra
3. Restricted Estimation
4. Hypothesis Testing
5. Nonlinear Least Squares
6. Instrumental Variables
7. Generalized Method of Moments
8. Panel Data Model
9. Quantile Regression
10. Discrete Choice Model
11. Kernel Density Estimation
12. Nonparametric Regression
13. Series Estimation
14. Bootstrap Methods
The course grade will be based on
1. Participation: 20%
2. Presentation: 40%
3. Problem Sets: 40%
The presentation assignment depends on the class size and will be discussed in the first class. The problem sets will include both problem solving and data analysis exercises, and STATA is recommended for data analysis exercises. No Late assignments will be accepted. If you fail to turn in homework, you will receive a zero for that homework. Students are encouraged to work with others in the class on homework, but each student must write up his/her own solutions.
Required textbook:
Bruce Hansen (2022), Econometrics, Princeton University Press.
https://www.ssc.wisc.edu/~bhansen/econometrics/
Supplementary books:
Joshua D. Angrist and Jorn-Steffen Pischke (2009), Mostly Harmless Econometrics: An Empiricist's Companion.
William H. Greene (2018), Econometric Analysis}, 8th edition, Pearson Higher Education.
Jeffrey Wooldridge (2019), Introductory Econometrics: A Modern Approach, 7th Edition, Cengage Learning.