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Graduate major in Industrial Engineering and Economics
Instructor(s)
Ogasawara Kota
Course component(s)
Lecture
Day/Period(Room No.)
Mon5-6(W936)  Thr5-6(W936)
Group
-
Course number
IEE.B405
Credits
2
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

Intercultural skills Communication skills Specialist skills Critical thinking skills Practical and/or problem-solving skills
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### 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.

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### 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.

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