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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
- Media-enhanced courses
- Day/Period(Room No.)
- Mon5-6(W936) Thr5-6(W936)
- Group
- -
- Course number
- IEE.B405
- Credits
- 2
- Academic year
- 2019
- Offered quarter
- 1Q
- Syllabus updated
- 2019/4/1
- 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
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 I: CEF, Best predictor, Linear projection model | |
| Class 3 | Review II: OLSE, Unbiasedness | |
| Class 4 | Large sample asymptotics I | |
| Class 5 | Large sample asymptotics II | |
| Class 6 | Large sample asymptotics III | |
| Class 7 | Asymptotic theory for least squares I | |
| Class 8 | Asymptotic theory for least squares II | |
| Class 9 | Asymptotic theory for least squares III | |
| Class 10 | Asymptotic theory for least squares IV | |
| Class 11 | Endogeneity I | |
| Class 12 | Endogeneity II | |
| Class 13 | Panel data | |
| Class 14 | Review | |
| Class 15 | Final |
Textbook(s)
Textbook: Bruce E. Hansen, Econometrics, University of Wisconsin, 2018.
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 strongly recommended 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
