シラバス参照
シラバス参照
| 科目一覧へ戻る | 2022/04/06 現在 |
| 科目名／Subject | 実証研究入門 |
|---|---|
| 担当教員（所属）／Instructor | 小野塚 祐紀 (商学部) |
| 授業科目区分／Category | 昼間コース 学科別専門科目 |
| 開講学期／Semester | 2022年度／Academic Year 後期／Fall Semester |
| 開講曜限／Class period | 月/Mon 4 , 月/Mon 5 |
| 対象所属／Eligible Faculty | 商学部／Faculty of Commerce |
| 配当年次／Years | 2年 , 3年 , 4年 |
| 単位数／Credits | 2 |
| 研究室番号／Office | 小野塚 祐紀(1号館441室) |
| オフィスアワー／Office hours |
小野塚 祐紀(前期：月曜15:00-16:00 後期：月曜13:00-14:00) |
| 更新日／Date of renewal | 2022/03/01 |
|---|---|
| 授業の目的・方法 ／Course Objectives and method |
This course is intended to learn basic knowledge and skills on causal inference. In the first part of the course we will review basic knowledge of statistics/econometrics because the knowledge is necessary for an understanding of methods for causal inference. In the second part of the course, we will cover major methods for causal inference: RCT, RDD, DID, and propensity score matching. |
| 達成目標 ／Course Goals |
Students will 1) review basic statistics and econometrics 2) understand differences between spurious correlation and causal relationship 3) acquire basic knowledge and skills on major methods for causal inference |
| 授業内容 ／Course contents |
Week 1 (1, 2): Introduction & Review of probability Week 2 (3, 4): Review of statistics & Simple linear regression (part 1) Week 3 (5, 6): Simple linear regression (part 2) & “R” Week 4 (7,8): Randomized Control Trial Week 5 (9, 10): Regression Discontinuity Design Week 6 (11, 12): Difference-in-Differences design Week 7 (13, 14): Propensity score matching Week 8 (15): Review |
| 事前学修・事後学修 ／Preparation and review lesson |
Quizzes. Students will need to review class materials. |
| 使用教材 ／Teaching materials |
No textbook, but the lectures of the first part will be based on Ch.2-5 of “Introduction to Econometrics” by J. H. Stock & M. W. Watson. Also, the following website might be useful for the second part: https://mru.org/mastering-econometrics . |
| 成績評価の方法 ／Grading |
35%: 7 Quizzes (5% each; probability, statistics, linear regression, RCT, RDD, DID, propensity score matching) 15%: Contribution in class 50%: Final exam (the methods for causal inference) |
| 成績評価の基準 ／Grading Criteria |
100-90 秀 89-80 優 79-70 良 69-60 可 0-59 不可 |
| 履修上の注意事項 ／Remarks |
Having basic knowledge of statistics and econometrics is not required but strongly recommended for your better understanding of this course. This course will be provided in English, but Japanese may be supplementary used depending on the situation. The schedule is subject to change. |
| 実務経験者による授業 ／Courses conducted by the ones with practical experiences |
該当しない |