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- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Industrial Engineering and Economics
- Instructor(s)
- Ogasawara Kota
- Class Format
- Lecture (ZOOM)
- Media-enhanced courses
- Day/Period(Room No.)
- Mon5-6(W936) Thr5-6(W936)
- Group
- -
- Course number
- IEE.B405
- Credits
- 2
- Academic year
- 2020
- Offered quarter
- 1Q
- Syllabus updated
- 2020/9/18
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
This course is designed for 1st year graduate students and is taught in English.
Student learning outcomes
The course aims to present and illustrate the theory and techniques of modern econometric analysis.
Keywords
Least square regression, Large sample asymptotics, Endogeneity, Panel data analysis
Competencies that will be developed
| ✔ Specialist skills | ✔ Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
The first part begins with reviews of the conditional expectation and least square regression. The second part introduces the large sample asymptotics. The third part applies the large sample asymptotics to the least squares. The final part introduces concepts of endogeneity.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Orientation and introduction | Orientation and introduction |
| Class 2 | Review II: CEF, Best predictor, Linear projection model | Review I: CEF, Best predictor, Linear projection model |
| Class 3 | Review II: OLSE and Normal regression model | Review II: OLSE and Normal regression model |
| Class 4 | Large sample asymptotics I | Large sample asymptotics I |
| Class 5 | Large sample asymptotics II | Large sample asymptotics II |
| Class 6 | Large sample asymptotics III | Large sample asymptotics III |
| Class 7 | Asymptotic theory for least squares I | Asymptotic theory for least squares I |
| Class 8 | Asymptotic theory for least squares II | Asymptotic theory for least squares II |
| Class 9 | Endogeneity I: Causality and Two-stage least squares | Endogeneity I: Causality and Two-stage least squares |
| Class 10 | Endogeneity II: Panel data analysis I | Endogeneity III: Panel data analysis I |
| Class 11 | Endogeneity III: Panel data analysis II | Endogeneity III: Panel data analysis II |
| Class 12 | Empirical examples | Empirical examples |
| Class 13 | Review | Review |
| Class 14 | Exercise | Exercise |
Out-of-Class Study Time (Preparation and Review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Textbook: Bruce E. Hansen, Econometrics, University of Wisconsin, 2020.
Reference books, course materials, etc.
None
Assessment criteria and methods
Problem solving or midterm 30%, Final exam 70%.
Related courses
- IEE.B207 ： Econometrics I
- IEE.B301 ： Econometrics II
- IEE.B334 ： Cliometrics
- IEE.A204 ： Probability for Industrial Engineering and Economics
- IEE.A205 ： Statistics for Industrial Engineering and Economics
Prerequisites (i.e., required knowledge, skills, courses, etc.)
The course prerequisites are Econometrics I (level: IEE 200) and Econometrics II (level: IEE 300). I recommend both Introductory Courses in Statistics and Probability (level: IEE 200) by Professor Masami Miyakawa and Cliometrics (level: IEE 300) by Professor Daisuke Kurisu as the prerequisites. Students should be familiar with basic concepts in probability and statistical inference. Familiarity with matrix algebra is preferred.
Other
None
