Type of Credit: Elective
Credit(s)
Number of Students
Econometrics is a discipline grounded in advanced economic theories and various quantitative methods. It focuses on empirical tasks of analyzing observational data, such as estimating causal effects, testing economic theories, evaluating and implementing government and business policies, and forecasting key macroeconomic indicators.
Econometrics has become indispensable for economists conducting serious empirical research. This one-semester course is designed to introduce graduate-level students to well-established econometric tools, particularly regression-based methods for handling cross-sectional data.
The course will cover the following topics:
Course introduction: modern Econometrics and causal inference
Multiple regression analysis: estimation
Multiple regression analysis: inference
Multiple regression analysis: OLS asymptotics
Multiple regression analysis: further issues
Multiple regression analysis with qualitative information
Heteroskedasticity
Instrumental variables estimation and two stage least squares
Limited dependent variable Models and sample selection corrections
Instructional Method: Lecture
Course Requirements:
Students should have a solid understanding of undergraduate-level statistics, calculus, linear algebra, and economics before enrolling in the course. Regular attendance at lectures and completion of assigned exercises are required.
Please note that the course will begin with multiple regression analysis rather than simple regression analysis. Therefore, students who are not familiar with simple regression analysis (or the relevant statistical tools) are encouraged to review these concepts independently to ensure they can keep up with the course material.
能力項目說明
By the end of the course, students will be equipped with the econometric and statistical techniques necessary for empirical analysis, which will be invaluable in their future careers, whether in industry or academia.
| 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 |
|---|---|---|
| 周次 | 課程主題 | 課程內容與指定閱讀 | 教學活動與作業 | 學習投入時數 | |
| 1 | Course Introduction | 課程投影片 | 簡介課程: 現代計量經濟學及因果推論 | 1.5 | |
| 2 | Multiple regression analysis: estimation | 課程投影片, Chapters 3 of Wooldridge (2019) | 多變量迴歸分析: 估計 | 4 | |
| 3 | Multiple regression analysis: estimation | 課程投影片, Chapters 3 of Wooldridge (2019) | 多變量迴歸分析: 估計 | 4 | |
| 4 | Multiple regression analysis: inference | 課程投影片, Chapters 4 of Wooldridge (2019) | 多變量迴歸分析: 統計推論 | 4 | |
| 5 | Multiple regression analysis: inference | 課程投影片, Chapters 4 of Wooldridge (2019) | 多變量迴歸分析: 統計推論 | 4 | |
| 6 | Multiple regression analysis: OLS asymptotics | 課程投影片, Chapters 5 of Wooldridge (2019) | 多變量迴歸分析: 漸進性質 | 4 | |
| 7 | Multiple regression analysis: further issues | 課程投影片, Chapters 6 of Wooldridge (2019) | 多變量迴歸分析: 其他課題 | 4 | |
| 8 | Multiple regression analysis with qualitative information | 課程投影片, Chapters 7 of Wooldridge (2019) | 多變量迴歸分析: 質性資料 | 4 | |
| 9 | Midterm exam week | Midterm exam | 無 | 3 | |
| 10 | Heteroskedasticity | 課程投影片, Chapters 8 of Wooldridge (2019) | 異質性 | 4 | |
| 11 | Heteroskedasticity | 課程投影片, Chapters 8 of Wooldridge (2019) | 異質性 | 4 | |
| 12 | Instrumental variables estimation and two stage least squares | 課程投影片, Chapters 15 of Wooldridge (2019) | 工具變數及二階段OLS法 | 4 | |
| 13 | Instrumental variables estimation and two stage least squares | 課程投影片, Chapters 15 of Wooldridge (2019) | 工具變數及二階段OLS法 | 4 | |
| 14 | Limited dependent variable Models and sample selection corrections | 課程投影片, Chapters 17 of Wooldridge (2019) | 限制性因變數模型及樣本選擇調整 | 4 | |
| 15 | Limited dependent variable Models and sample selection corrections | 課程投影片, Chapters 17 of Wooldridge (2019) | 限制性因變數模型及樣本選擇調整 | 4 | |
| 16 | Final Exam week | Final exam | 無 | 3 | |
| 17 | 自主總整學習 | 自主總整學習 | 無 | ||
| 18 | 自主總整學習 | 自主總整學習 | 無 |
Exercises (30%), midterm exam (30%), final exam (30%), attendance at lectures (10%).
Exercises will be assigned after each lecture topic ends. Some of these exercises will involve computer-based tasks, and solutions will be provided in Python code. However, students are welcome to use any statistical software of their choice to complete the assignments.
Requirements for exercises:
- No late submission is allowed (no matter what reasons you have).
- For computer exercises, relevant program codes should be attached.
- Submit your exercises on the Moodle.
Required textbook:
Wooldridge, Jeffrey M. (2019): "Introductory Econometrics: A Modern Approach", 7th Edition, Cengage Learning.
The main course will follow chapters 3 - 8, 15 and 17 of the book.