Econometrics

Econometrics

専門教育科目

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国際商経学部

2年

KCCBG2MCA7

2単位

講義・演習 (Lecture/Seminar)

2025年度前期

(Spring semester)

Jean-Baptiste M.B. SANFO

国際商経学部

英語

目標9

Before or after class, in the classroom

1◎／2〇／3〇

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【Course Objectives】
This course provides an introduction to econometrics. It will give students a basic understanding of econometric concepts and equip them with the necessary skills to conduct rigorous empirical research.  It will focus on econometric applications to analyze economic data and make informed decisions in economics, business, and other settings.

In this course, using the R software, students will gain hands-on experience in data analysis and enhance their skills in interpreting and critically evaluating empirical findings. This course will be valuable for their graduation thesis research and also for actual decision making in this data-driven world.

【Learning Outcome】
By the end of this course, students will be able to:
1. Demonstrate understanding the foundations of econometrics.
2. Apply econometric methods to real-world economic data.
3. Conduct hypothesis tests and interpret the results.
4. Utilize R software to analyze data.
5. Critically evaluate and interpret empirical research results

Ordinary least square, hypothesis testing, causality, policy analysis

【Course Overview】
I will first present each topic theoretically to ensure an understanding of the necessary  fundamental concepts and terminology. Then, I will provide real-world data, and together we will estimate models in R and discuss the results.

【Schedule】
Lecture 1: Guidance and Review of Basic Statistical Concepts
Lectures 2 - 3: The Simple Regression Analysis
Lecture 4:  Multiple Regression Analysis: Estimation
Lecture 5:  Multiple Regression Analysis: Inference
Lecture 6:  Multiple Regression Analysis: OLS Asymptotics
Lecture 7:  Mid-term evaluation
Lecture 8-9: Regression Analysis with Binary or Dummy variables
Lectures 10-11:   Regression Analysis with Pooled Cross sections Data
Lectures 12 - 13:  Regression Analysis with Panel Data
Lectures 14- 15:   Basic Regression Analysis with Time Series Data

Final evaluation

The content of the course may vary depending on the pace of the progress.

Policy on the Use of Generative AI:

Students are expected to follow the instructions of the instructor regarding the use of generative AI. While it is acceptable to use generative AI as a supplementary tool for activities such as writing reports and conducting case searches, it is crucial to verify the accuracy of the content produced. Students should ensure that facts are checked and that sources and references are properly acknowledged. The output from generative AI must not be submitted as an assignment without the necessary changes. If this practice is discovered, credit may be denied or revoked.

Introductory Econometrics: A Modern Approach, by Jeffrey M. Wooldridge

H. Stock & M. M. Watson Introduction to Econometrics 4th ed. Pearson
Econometrics for Business Analytics by Jose Fernandez
https://bookdown.org/cuborican/RE_STAT/
Introduction to Econometrics with R by Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer
https://www.econometrics-with-r.org/

【Pre-Learning】Students will be provided with lecture notes and handouts beforehand. Please read them before the class.

【Post-Learning】Lecture notes and handouts will contain exercises or questions. Please review them after the class.

Students will be asked to share their understanding of given concepts or asked to report orally what they discussed in groups.

Econometric models will be estimated in class using real-world data. We will discuss estimation results together.

Engagement in classroom activities (20%)
Assignments (20%)  
Mid-term Evaluation (30%)
Final evaluation (30%)

In the classroom

The maximum number of students is 60. If the number of applicants exceeds 60, students will be selected by lottery. Students who wish to take the course must register for the lottery via the Universal Passport, after the lottery results have been announced, please register for the winning course on your own.

The prerequisite for this course is Introductory Statistics for Economics.

Regular attendance is required.

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Remember that making mistakes is a natural part of the learning process. Don’t fear mistakes; instead, view them as valuable opportunities for growth. Be proactive in your learning by seeking help, asking questions, and sharing your thoughts. Learning is a collaborative effort, and I’m here to support you every step of the way. Just as you are learning, so am I; together, we can create a positive and enriching experience. Let’s engage, explore, and learn from each other!