### 2020　Advanced Econometrics

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

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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 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.

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