This course is designed for 1st year graduate students and is taught in English.
The course aims to present and illustrate the theory and techniques of modern econometric analysis.
Least square regression, Large sample asymptotics, Endogeneity, Panel data analysis
|✔ Specialist skills||✔ Intercultural skills||Communication skills||Critical thinking skills||✔ Practical and/or problem-solving skills|
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|
|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|
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: Bruce E. Hansen, Econometrics, University of Wisconsin, 2020.
Problem solving or midterm 30%, Final exam 70%.
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.