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
|✔ 専門力||✔ 教養力||コミュニケーション力||展開力(探究力又は設定力)||✔ 展開力(実践力又は解決力)|
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.
|第1回||Orientation and introduction||Orientation and introduction|
|第2回||Review I: CEF, Best predictor, Linear projection model||Review I: CEF, Best predictor, Linear projection model|
|第3回||Review II: OLSE and Normal regression model||Review II: OLSE and Normal regression model|
|第4回||Large sample asymptotics I||Large sample asymptotics I|
|第5回||Large sample asymptotics II||Large sample asymptotics II|
|第6回||Large sample asymptotics III||Large sample asymptotics III|
|第7回||Asymptotic theory for least squares I||Asymptotic theory for least squares I|
|第8回||Asymptotic theory for least squares II||Asymptotic theory for least squares II|
|第9回||Endogeneity I: Causality and Two-stage least squares||Endogeneity I: Causality and Two-stage least squares|
|第10回||Endogeneity II: Panel data analysis I||Endogeneity III: Panel data analysis I|
|第11回||Endogeneity III: Panel data analysis II||Endogeneity III: Panel data analysis II|
|第12回||Empirical examples||Empirical examples|
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.