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授業情報/Course information

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

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