科目ナンバー

GM-OM2-SB01

履修条件・関連科目等

Students should have passed Introductory economics (Macroeconomics) and Microeconomics.

授業で使用する言語

英語

授業で使用する言語（その他の言語）

授業の概要

In the seminars, students can pursue economic, firm, and managerial issues that they like. Students will learn how to self-identify specific problems and propose the corresponding solutions based on evidence found from data analyses. Students start with basic statistics, economic reports, and academic papers. Students also learn how to analyze the data, do estimations, and report the results. Students study applied methods together with reading papers on a wide range of economic issues. Then, students choose a topic of their interest, search for the literature, raise their own hypothesis, seek data to test the hypothesis, analyze the data and report the results. By learning step-by-step, with the accumulated knowledge and skills, students can write meaningful and high-quality graduation thesis (project).

Language including graduation thesis (research project) is English. It is preferable to use quantitative methods and microdata to answer the research questions. Learning how to use statistical software gradually is a must.

Specifically, in the seminar I, students will study/review basic statistics, read and analyze economic papers.

科目目的

The course is to help students expand their knowledge on economic issues of their interest and basic tools for doing proper data analyses.

到達目標

By the end of the course, students should be able to identify economic problems and how they are solved in the previous studies. Students should be able to find, do presentations, and comments on academic papers.

授業計画と内容

Course content & schedule (subject to change):

1. Introduction: Knowing your big goals.
2. How to (1): Read academic papers and find add-on values. Review basic statistics (1).
3. How to (2): Do an academic presentation. Review basic statistics (2).
4. How to (3): Search for academic papers. How trustworthy is the source. R studio.
5. Data and variables. Application using R studio.
6. Correlation and causation. Data description and descriptive statistics.
7. Individual presentations on academic papers. Data visualization.
8. Individual presentations on academic papers. What is a research topic?
9. Individual presentations on academic papers. How to find a topic to do research?
10. Individual presentations on academic papers. How to make research questions?
11. Individual presentations on academic papers. From a topic to sources of data.
12. Individual presentations on academic papers. How to run a simple regression.
13. Individual presentations on academic papers. Hypothesis testing.
14. Individual presentations on academic papers. Basic causal inference.

授業時間外の学修の内容

指定したテキストやレジュメを事前に読み込むこと

授業時間外の学修の内容（その他の内容等）

授業時間外の学修に必要な時間数／週

・毎週１回の授業が半期（前期または後期）または通年で完結するもの。１週間あたり４時間の学修を基本とします。
・毎週２回の授業が半期（前期または後期）で完結するもの。１週間あたり８時間の学修を基本とします。

成績評価の方法・基準

成績評価の方法・基準（備考）

課題や試験のフィードバック方法

授業時間内で講評・解説の時間を設ける

課題や試験のフィードバック方法（その他の内容等）

アクティブ・ラーニングの実施内容

グループワーク／プレゼンテーション

アクティブ・ラーニングの実施内容（その他の内容等）

授業におけるICTの活用方法

実施しない

授業におけるICTの活用方法（その他の内容等）

実務経験のある教員による授業

いいえ

【実務経験有の場合】実務経験の内容

【実務経験有の場合】実務経験に関連する授業内容

テキスト・参考文献等

Textbook
Mesquita and Flower. (2021). "Thinking clearly with data. A guide to quantitative reasoning and analysis". Princeton Univ. Press.

Reference books
1. Jonathan Schwabish. (2017). “Better Presentations: A guide for scholars, researchers, and wonks”. Columbia University Press: New York.
2. FitzGerald. (2016). “The craft of research. Fourth edition”. The University of Chicago Press: Chicago. (Free access: https://ebookcentral.proquest.com/lib/chuouniv-ebooks/detail.action?docID=4785166&query=The%20craft%20of%20research)
3. Rosenbaum. (2023). "Causal Inference". MIT press.

その他特記事項

Presentations, lecture, discussion, and graduation thesis (research project) are entirely in English. It is preferable to use quantitative methods and microdata to answer the research questions. Learning how to use statistical software gradually is a must.
Class attendance is mandatory.

参考URL

Seminar's page

https://sites.google.com/g.chuo-u.ac.jp/tienmanhvu/prologue?authuser=0

Instructor's site

https://sites.google.com/view/tienmanhvu/home?authuser=0