シラバス参照

授業情報/Course information

科目一覧へ戻る 2024/01/31 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 2023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 指定なし
配当年次/Years 2年,3年,4年
単位数/Credits 0.0
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 2023/02/28
授業の目的・方法
/Course Objectives and method
This course is intended to learn basic knowledge on causal inference. We will cover major methods for causal inference: IV, RCT, RDD, DID. Lectures will mostly follow Angrist & Pischke (2015).
達成目標
/Course Goals
Students will
1) understand differences between spurious correlation and causal relationship
2) acquire basic knowledge on major methods for causal inference
授業内容
/Course contents
Week 1 (1, 2): Introduction, Causal effect (first half of Ch.1 of Angrist & Pischke, 2015)
Week 2 (3, 4): Inference (Ch.1 Appendix)
Week 3 (5, 6): Linear regression
Week 4 (7,8): Randomized Control Trial (second half of Ch.1)
Week 5 (9, 10): Instrumental Variable method (Ch.3)
Week 6 (11, 12): Regression Discontinuity Design (Ch.4)
Week 7 (13, 14): Difference-in-Differences (Ch.5)
Week 8 (15): Review
事前学修・事後学修
/Preparation and review class
Quizzes.
Students will need to review class materials.
使用教材
/Teaching materials
Joshua Angrist & Jorn-Steffen Pischke. (2015). "Mastering 'Metrics: The Path from Cause to Effect". Princeton University Press.
成績評価の方法
/Grading
35%: 7 Quizzes (5% each)
15%: Contribution in class
50%: Final exam
成績評価の基準
/Grading Criteria
100-90 A
89-80 B
79-70 C
69-60 D
0-59 F
履修上の注意事項
/Remarks
Having basic knowledge of statistics and econometrics is 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/No
授業実施方法
/Method of class
①面接授業/Face-To-Face class
遠隔授業
/Online class
遠隔授業/Online class

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