Type of Credit: Elective
Credit(s)
Number of Students
Econometrics is a discipline based upon advanced economic theories and various quantitative methods. It focuses on the problems inherent in collecting and analyzing observational data, such as empirically estimating economic relationships, testing economic theories, evaluating and implementing government and business policies, and forecasting important macroeconomic indicators, etc.
Econometrics now is indispensable for an economist to conduct serious empirical research. The one-semester course aims to introduce graduate level students some well-established econometric methods, in particular those for dealing with cross-sectional data.
The course will cover the following topics:
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
Simultaneous equations models
Limited dependent variable Models and sample selection corrections
Instructional Method: Lecture
Course Requirements:
Students need to be familiar with undergraduate level statistics, calculus and economics before taking the course. Students are required to attend lectures and finish assigned exercises.
Note that the course will start from multiple regression analysis rather than simple regression analysis. Therefore students who are not familiar with simple regression analysis (or relevant statistical tools) need to study it by themselves in order to catch up with the course.
能力項目說明
After learning the course, students are expected to know how to use econometric and statistical techniques in empirical analyses, which will be extremely useful for their future careers in the industry or study.
| 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，計量經濟學本質及經濟資料 | 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 | Simultaneous equations models | 課程投影片, Chapters 16 of Wooldridge (2019) | 聯立方程式模型 | 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 | 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 |
| 17 | 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 |
| 18 | Final Exam week | Final exam | 無 | 3 |
Exercises (30%), midterm exam (30%), final exam (40%)
The exercises will be assigned after one lecture topic ends. Some of the exercises are computer exercises and their solutions will be given in Python codes. However, students are free to use any statistical software to solve them.
Some requirements for the exercises:
- No late submission is allowed (no matter what reasons you have).
- For computer exercises, relevant program codes should be attached.
- Put your name, student ID. number and department on the submitted exercises.
- The submitted exercises should be bound at staples or with paper clips.
Required textbook:
Wooldridge, Jeffrey M. (2019): "Introductory Econometrics: A Modern Approach", 7th Edition, Cengage Learning.
The main course will follow chapters 3 - 8 and 15 - 17 of the book.