2020 Faculty Courses School of Engineering Undergraduate major in Industrial Engineering and Economics
Econometrics I
- Academic unit or major
- Undergraduate major in Industrial Engineering and Economics
- Instructor(s)
- Kota Ogasawara
- Class Format
- Lecture (Zoom)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Tue (W934) / 5-6 Fri (W934)
- Class
- -
- Course Code
- IEE.B207
- Number of credits
- 200
- Course offered
- 2020
- Offered quarter
- 3Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
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.
Course description and aims
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
Competencies
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Orientation and introduction | Orientation and introduction |
| Class 2 | Basic concepts I: Conditional expectation function | Basic concepts I: Conditional expectation function |
| Class 3 | Basic concepts II: Properties of the conditional expectation | Basic concepts II: Properties of the conditional expectation |
| Class 4 | The linear projection model | The linear projection model |
| Class 5 | The algebra of least squares I: Least squares estimator | The algebra of least squares I: Least squares estimator |
| Class 6 | The algebra of least squares II: Least squares estimator | The algebra of least squares II: Least squares estimator |
| Class 7 | The algebra of least squares III: FWL theorem | The algebra of least squares III: FWL theorem |
| Class 8 | Finite-sample properties of the OLSE | Finite-sample properties of the OLSE |
| Class 9 | Normal regression model and MLE | Normal regression model and MLE |
| Class 10 | Endogeneity I: Concept of causal effect | Endogeneity I: Concept of causal effect |
| Class 11 | Endogeneity II: Two-stage least squares | Endogeneity II: Two-stage least squares |
| Class 12 | Empirical examples | Empirical examples |
| Class 13 | Review | Review |
| Class 14 | Exercise | Exercise |
Study advice (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)
Bruce E. Hansen. Econometrics. University of Wisconsin, 2020 (Chapters 1--5).
Reference books, course materials, etc.
A recommended supplementary monograph is Mastering Metrics by Joshua D. Angrist & Jorn-Steffen Pischle.
Evaluation methods and criteria
Problem solving or midterm 30%, final exams 70%.
Related courses
- IEE.A205 ： Statistics for Industrial Engineering and Economics
- IEE.A204 ： Probability for Industrial Engineering and Economics
- IEE.B301 ： Econometrics II
- IEE.B405 ： Advanced Econometrics
Prerequisites
I recommend 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.