授業の目的 【日本語】
Goals of the Course(JPN) | | 本講義は、空間計量経済学、因果推論、パネルおよび動学モデル、ベイズ計量経済学、機械学習といった現代的な数量経済分析の主要分野を体系的に学ぶことを目的とする。空間データサイエンスの基礎、空間依存性やスピルオーバー効果、空間的不均質性およびスケール効果を扱うとともに、処置効果の推定、マッチングや二重にロバストな手法、異質的差分の差分法、合成コントロール法などの因果推論手法を学ぶ。さらに、静学および動学パネルデータ分析、共通因子モデルや潜在グループ分析を取り上げ、ベイズ統計・ベイズ計量経済学、モデル不確実性、ベイズモデル平均化について理解を深める。最後に、予測と解釈のための機械学習手法、因果推論におけるダブル・マシンラーニング、処置効果の異質性分析を学ぶ。
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授業の目的 【英語】
Goals of the Course | | This course provides an overview of key areas of modern quantitative economics, covering spatial econometrics, causal inference, panel and dynamic modeling, Bayesian econometrics, and machine learning. Students study foundations of spatial data science, spatial dependence and spillover effects, and spatial heterogeneity and scale effects, alongside core causal inference methods such as treatment effect estimation, matching, doubly robust approaches, heterogeneous differences-in-differences, and synthetic control methods. The course further examines static and dynamic panel data models, common factors, and latent group structures, and introduces Bayesian statistics, model uncertainty, and Bayesian model averaging. Finally, students are exposed to machine-learning methods for prediction, interpretation, and causal analysis, including double machine learning and the study of heterogeneous treatment effects.
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到達目標 【日本語】
Objectives of the Course(JPN) | | * 経済・社会経済データ（クロスセクション、パネル、空間データ）を対象として、空間計量経済学、因果推論、-- パネル分析、ベイズ計量経済学、機械学習の手法を用い、パターンの把握、関係性の推定、実証的な知見の抽出ができるようになる。
- 静学および動学パネルモデル、因果推論手法、空間計量経済モデル、機械学習手法を、標準的な計量・統計ソフトウェアおよびプログラミング環境を用いて実装・分析できるようになる。
- 空間依存性および空間的不均質性、処置効果の異質性、ベイズ推論とモデル不確実性、予測および因果分析のための機械学習手法など、現代的な研究手法について理論的理解を深める。
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到達目標 【英語】
Objectives of the Course | | - Apply spatial, causal, panel, Bayesian, and machine-learning methods to identify patterns, estimate relationships, and extract insights from cross-sectional, panel, and spatially structured socioeconomic data.
- Implement static and dynamic panel models, causal inference techniques, spatial econometric models, and machine-learning methods using standard econometric and statistical software and programming environments.
- Develop a conceptual understanding of contemporary research methods, including spatial dependence and heterogeneity, treatment effect heterogeneity, Bayesian inference and model uncertainty, and machine-learning approaches for prediction and causal analysis.
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授業の内容や構成
Course Content / Plan | | 1. Introduction and overview
Topics in spatial econometrics
2. Foundations of spatial data science
3. Spatial dependence and spillover effects
4. Spatial heterogeneity and scale effects
Topics in causal inference
5. Treatment effects, matching, and doubly robust estimation
6. Heterogeneous differences in differences
7. Synthetic control methods
Topics in panel and dynamic modeling
8. Static panel data analysis
9. Dynamic panel data analysis
10. Common factors and latent groups
Topics in Bayesian econometrics
11. Bayesian statistics and econometrics
12. Model uncertainty and Bayesian model averaging
Topics in machine learning
13. Machine learning: Prediction and interpretation
14. Double machine learning for causal inference
15. Heterogeneous treatments and machine learning |
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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 | | | Research project and video presentation. 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 | | - Cameron ,A. (2022) Analysis of Economics Data: An Introduction to Econometrics. Slides, Videos, Data, and Code: http://cameron.econ.ucdavis.edu/aed/. [Cheap kindle book](https://www.amazon.co.jp/-/en/Colin-Cameron-ebook/dp/B09Q9CT5GN/ref=sr_1_1?keywords=ANALYSIS+OF+ECONOMICS+DATA%3A+AN+INTRODUCTION+TO+ECONOMETRICS&qid=1667440528&qu=eyJxc2MiOiIwLjgxIiwicXNhIjoiMC44MSIsInFzcCI6IjAuODEifQ%3D%3D&sr=8-1)
- Wooldridge, J. (2020) Introductory Econometrics: A Modern Approach, 7Edition. CENGAGE, Asia Edition. ISBN-13: 9789814866088. Ebook available here: https://ebookcentral.proquest.com/lib/nagoyauniv/detail.action?docID=6351340
- Söderbom, M., Teal, F., Eberhardt, M., Quinn, S., & Zeitlin, A. (2014). *Empirical development economics*. Routledge. Ebook available here: https://ebookcentral.proquest.com/lib/nagoyauniv/detail.action?docID=1811011
- Elhorst, P. (2014). *Spatial econometrics from cross-sectional data to spatial panels*
Springer. Ebook available here: https://ebookcentral.proquest.com/lib/nagoyauniv/detail.action?docID=1466460
- Cameron, A. & Trivedi, P. (2022). Microeconometrics using Stata. College Station, Tex: Stata Press. Available in the GSID library
The following open ebooks are available for free on the internet
- Heiss, F. (2016). Using R for introductory econometrics. Ebook available at http://www.urfie.net/
- Heiss, F. and Brunner, D. (2020). Using Python for introductory econometrics. Ebook a vailable at http://www.upfie.net/
The following ebooks are available when using the internet of Nagoya University.
- 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
- Dayal, V. (2020). Quantitative economics with R : A data science approach. Singapore: Springer. Ebook: https://ebookcentral.proquest.com/lib/nagoyauniv/detail.action?docID=6112508
- 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 |
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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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