授業の目的 【日本語】
Goals of the Course(JPN) | | | この講座は、数量経済分析のための古典的で新興の手法の概要を提供します。古典的な回帰分析の手法を通して、学生は横断面データやパネルデータなど、さまざまな種類の社会経済データからパターンを明らかにし、洞察を得ることができるようになります。基本的な数量方法の理解を深めた後、学生はR、Stata、Geoda、およびPythonなど、さまざまなソフトウェアパッケージやプログラミング言語を使用してこれらの方法を適用する方法を学びます。より現代的な数量方法の視点から、学生は空間計量モデリングや因果推論に基づく基本的な理解を深めます。 |
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授業の目的 【英語】
Goals of the Course | | | This course provides an overview of both classical and emerging new methods for quantitative economic analysis. Through the lens of classical regression methods, students will be able to uncover patterns and extract insights from multiple types of socioeconomic data, including cross-sectional and panel datasets. After developing a basic understanding of the quantitative methods, students will learn how to apply them using various software packages and programming languages, including R, Stata, Geoda, and Python. Through the lens of more modern quantitative methods, students will develop a basic understanding of spatial econometrics modeling. The course also provides supplementary online lectures and tutorials on principles of statistics, regression analysis, and statistical programming for those students who need them. |
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到達目標 【日本語】
Objectives of the Course(JPN) | | -統計的手法を使用して、さまざまな社会経済データからパターンを明らかにすることができます。
-複数のソフトウェアパッケージとプログラミング言語を使用して、断面、時系列、およびパネルデータセットを分析できます。
-空間計量経済学、マルコフ連鎖モデリング、機械学習の分野における最新の研究方法のいくつかを理解します。 |
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到達目標 【英語】
Objectives of the Course | | - Use statistical methods to uncover patterns and extract insights from multiple types of socioeconomic data.
- Be able to use multiple software packages and programming languages to analyze cross-section and panel datasets.
- Understand some of the latest research methods in the field of spatial econometrics. |
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授業の内容や構成
Course Content / Plan | | 01 Introduction and overview
(A) Topics in panel data and nonlinearities
02. Static panel data analysis
03. Probit and logit models
(B) Topics in causal inference
04. Instrumental variables
05. Regression discontinuity desings
06. Advanced Differences in differences
07. Synthetic control methods
(C) Topics in spatial data science
08. Foundations of spatial data science
09. Spatial dependence and spillover effects
10. Case studies and research projects
(D) Topics in Bayesian econometrics
11. Foundations of Bayesian statistics
12. Model uncertainty and Bayesian model averaging
13. Case studies and research projects
(E) Topics in machine learning
14. Foundations of machine learning
15. Case studies and research projects |
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履修条件・関連する科目
Course Prerequisites and Related Courses | | | Introduction to statistics and data science |
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成績評価の方法と基準
Course Evaluation Method and Criteria | | | Short research projects (at least 3). To receive credit for this course, students are expected to achieve an overall evaluation equal or superior to C- or C (where applicable). |
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教科書・参考書
Textbook/Reference Book | | - Wooldridge, J. (2020) AE Introductory Econometrics: A Modern Approach, 7Edition. CENGAGE, Asia Edition. ISBN-13: 9789814866088
- Cameron, A. & Trivedi, P. (2022). Microeconometrics using Stata. College Station, Tex: Stata Press.
The following open ebooks are available for free on the internet
- Heiss, F. (2016). Using R for introductory econometrics. Available at http://www.urfie.net/
- Heiss, F. and Brunner, D. (2020). Using Python for introductory econometrics. Available at http://www.upfie.net/
The following ebooks are available when using the internet of Nagoya University.
- Dayal, V. (2020). Quantitative economics with R : A data science approach. Singapore: Springer. Ebook: https://ebookcentral.proquest.com/lib/nagoyauniv/detail.action?docID=6112508
- Grekousis, G. (2020). Spatial Analysis Methods and Practice: Describe – Explore – Explain through GIS. Cambridge: Cambridge University Press. doi:10.1017/9781108614528. E-book: https://www.cambridge.org/core/books/spatial-analysis-methods-and-practice/4C135005A621335D06CC63EFF17E3913
- Mendez, C. (2020). Convergence Clubs in Labor Productivity and Its Proximate Sources: Evidence from Developed and Developing Countries. City-state: Springer. https://doi.org/10.1007/978-981-15-8629-3. E-book: https://ebookcentral.proquest.com/lib/nagoyauniv/detail.action?docID=6386038
A list of other online resources and tutorials is available at https://deepnote.com/@carlos-mendez |
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課外学習等（授業時間外学習の指示）
Study Load(Self-directed Learning Outside Course Hours) | | | - Students should create a (free) account in DISCORD(https://discord.com). Learning materials, problems sets, and other resources will be distributed via DISCORD. The invitation link to discord will be issued in the first class. |
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注意事項
Notice for Students | | | For further inquires about this course, send an email to carlos@gsid.nagoya-u.ac.jp. Office hours for consultations are available by appointment at https://carlos777.youcanbook.me |
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使用言語
Language(s) for Instruction & Discussion | | |
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授業開講形態等
Lecture format, etc. | | 対面で実施します。
Classes will be held in-person. |
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遠隔授業（オンデマンド型）で行う場合の追加措置
Additional measures for remote class (on-demand class) | | | - For online learning and communication purposes, we use Discord (https://discord.com) . Links to most of our learning materials are available on this website (access credentials to private channels are issued in the first week of each semester). |
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