授業の目的 【日本語】
Goals of the Course(JPN) | | | このコースでは、定量的研究のための応用計量経済学と新データ科学手法を紹介します。 古典的な回帰法に基づいて、学生は社会経済データからパターンを発見することができます。 定量的手法の基本を理解した後、学生はR、Stata、Geoda、およびPythonの使用方法を学びます。 新しいデータサイエンス手法に基づいて、学生は空間計量経済学、空間マルコフ連鎖モデリング、機械学習を学ぶことができます。 このコースでは、統計原理、回帰分析、および統計プログラミングが必要な学生向けに、補足のオンライン講義も提供しています。 |
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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, time series, 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, spatial Markov-chain modeling, and unsupervised machine learning. 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, time series, and panel datasets.
- Understand some of the latest research methods in the fields of spatial econometrics, Markov chain modeling, and machine learning. |
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授業の内容や構成
Course Content / Plan | | The structure of the course consists of 15 sessions divided into 3 broad topics. After each session students are expected to complete short research tasks and problem sets related to the main topics discussed in class.
01. Introduction and overview
Part I ) Regression analysis with cross-sectional data
02. Marginal effects, prediction, and interactions
03. Regression diagnostics
04. Instrumental variables and 2SLS estimation
05. Probit and logit models
Part II ) Regression analysis with time series and panel data
06. Finite and infinite distributed lag models
07. Unit roots, cointegration, Granger causality, and structural change
08. Polled models and synthetic control
09. Panel data and system GMM
Part III ) Advanced topics
10. Model uncertainty and robust predictors
11. Exploratory spatial data analysis
12. Spatial dependence and spillover effects
13. Spatial heterogeneity and multi-scale processes
14. System dynamics and Markov chain modeling
15. Introduction to machine learning |
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履修条件・関連する科目
Course Prerequisites and Related Courses | | | There is no precondition to take this course. However, a course in basic statistics and regression analysis is highly recommended. |
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成績評価の方法と基準
Course Evaluation Method and Criteria | | | Research tasks and problem sets (60%), final video presentation (40%). 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. (2010). 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 books and resources is available at https://www.facebook.com/groups/quaea |
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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) | | - Reading before each class is a necessity. Students should come to class prepared with questions and comments for discussion. A complete list of learning materials to be covered in this class is available at https://www.facebook.com/groups/quaea
- Students should have access to Nagoya University’s online learning management system NUCT: https://ct.nagoya-u.ac.jp/portal . Announcements, some learning resources, and grades will be posted in NUCT
- Students should create a (free) account in GO FORMATIVE (https://goformative.com/). A class access code will be provided during the first day of class.
- Students should create a (free) account in FlipGrid (https://flipgrid.com). A class access code will be provided during the first day of class. Most of the homework and problem sets will be delivered through FlipGrid.
- Students should create a (free) account in RStudio Cloud (https://rstudio.cloud). This computing environment will be used for some of the homework and problem sets of the 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. |
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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・遠隔（同時双方向型）の併⽤。遠隔授業は Teams、Zoom等で⾏う。
※履修登録後に授業形態等に変更がある場合には、NUCＴの授業サイトで案内します。
Combination of face-to-face and remote (interactive communication class) classes. Remote classes are conducted via Teams, Zoom, etc.
*Guidance will be posted on NUCT if there are any changes in the class format, etc. after registration. |
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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) | | |
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