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. Foundations of time series
07. Static panel data analysis
08. Dynamic panel data analysis
09. Synthetic control methods
Part III ) Advanced topics
10. Model uncertainty and robust predictors
11. Foundations of spatial data science
12. Classical and spatial Markov chains
13. Spatial heterogeneity and scale effects
14. Spatial spillovers and and diffusion effects
15. Foundations of 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 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.gg/NKb5GJJmkd). Learning materials, problems sets, and other resources will be distributed via DISCORD. The invitation link to discord will be issued in the first class.
- Students should create a (free) account in DEEPNOTE (https://deepnote.com/sign-in?token=ec5ee6cd). This computing environment will be used for some of the problem sets of the class. Here is a short overview of this powerful data science platform: https://pub.towardsai.net/introduction-to-deepnote-real-time-collaboration-on-jupyter-notebook-18509c95d62f
- Students should create a (free) student account in NOTION (https://www.notion.so/Notion-for-students-teachers-adc631df15ee4ab9a7a33dd50f4c16fe). The following video tutorial on how to use notion is highly recommended: https://youtu.be/ONG26-2mIHU
- Students should create a (free) student account in LOOM (https://www.loom.com/education). All students will explain the solutions to the problem sets through LOOM. |
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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 student consultation are on Wednesdays from 10:30 AM. Please book an 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. | | 原則として対面で行う。例外的に、遠隔授業（同時双方向）を行う。
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。
In principle, lecture and seminar course subjects are offered in-person.
Online participation in classes (interactive communication classes via Teams, Zoom, etc.) may be permitted by the instructor under exceptional circumstances.
*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) | | | - For online learning and communication purposes, we use the following discord server https://discord.gg/NKb5GJJmkd . 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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