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
|Intercultural skills||Communication skills||Specialist 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 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|
Textbook: Bruce E. Hansen, Econometrics, University of Wisconsin, 2018.
Problem solving or midterm 30%, Final exam 70%.
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.