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- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Undergraduate major in Industrial Engineering and Economics
- Instructor(s)
- Ogasawara Kota
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Mon7-8(W932) Thr7-8(W932)
- Group
- -
- Course number
- IEE.B301
- Credits
- 2
- Academic year
- 2019
- Offered quarter
- 1Q
- Syllabus updated
- 2019/4/1
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
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Course description and aims
This course is designed for 3rd or 4th year undergraduate students and is taught in Japanese. English language is used for the blackboarding in the preparation for Advanced Econometrics (IEE.B 405). Note that some students audit both Econometrics II and Advanced Econometrics in this quarter.
Student learning outcomes
The course aims to present and illustrate the theory and techniques of modern econometric analysis.
Keywords
Least square estimation, normal regression model, maximum likelihood estimation, nonlinear models, 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 concepts of the conditional expectation. The second part introduces concepts of the least square regression. The third part examines concepts of the normal regression model, maximum likelihood estimator, and a few nonlinear models. 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 | Basic concepts I: Conditional expectation function | |
| Class 3 | Basic concepts II: Properties of the conditional expectation | |
| Class 4 | The linear projection model | |
| Class 5 | The algebra of least squares I: Least squares estimator | |
| Class 6 | The algebra of least squares II: FWL theorem | |
| Class 7 | Finite-sample properties of the OLSE | |
| Class 8 | Normal regression model and MLE I | |
| Class 9 | Normal regression model and MLE II | |
| Class 10 | Nonlinear models | |
| Class 11 | Endogeneity I: Concept of causal effect | |
| Class 12 | Endogeneity II: Two-stage least squares | |
| Class 13 | Panel data | |
| Class 14 | Review | |
| Class 15 | Final |
Textbook(s)
Asano, S., and Nakamura, J. (2009). Econometrics. (in Japanese)
Reference books, course materials, etc.
I recommended Econometrics by Bruce E. Hansen as a supplement of the lecture notes (Chapter 1--5). A recommended supplementary monograph is Mastering Metrics by Joshua D. Angrist & Jorn-Steffen Pischle.
Assessment criteria and methods
Problem solving or midterm 30%, final exams 70%.
Related courses
- IEE.B207 ： Econometrics I
- IEE.B405 ： Advanced Econometrics
- IEE.B334 ： Cliometrics
- IEE.A204 ： Probability for Industrial Engineering and Economics
- IEE.A205 ： Statistics for Industrial Engineering and Economics
- IEE.B434 ： Advanced Topics in Econometrics
Prerequisites (i.e., required knowledge, skills, courses, etc.)
The course prerequisite is Econometrics I (level: IEE 200) by Professor Yoichiro Higuchi. I recommended Introductory Courses in Statistics and Probability (level: IEE 200) by Professor Masami Miyakawa as the prerequisites. Students should be familiar with basic concepts in probability and statistical inference. Familiarity with matrix algebra is preferred.
