Research Seminar 2 ｜ Syllabus data

Course Title

Research Seminar 2

Course Title in English

Research Seminar 2

Course Type

Major Courses

—

Eligible Students

School of Economics and Management

Target Grade

2Year

Course Numbering Code

KCCBG2MCA3

Credits

2.00Credits

The course numbering code represents the faculty managing the subject, the department of the target students, and the education category (liberal arts / specialized course). For detailed information, please download the separate manual from the upper right 'question mark'.

Type of Class

演習 (Seminar)

Eligible Year/Semester

Spring semester 2026

(Spring semester)

Instructor

Jean-Baptiste SANFO

Affiliation

School of Economics and Management

Language of Instruction

English

Related SDGs

9

Office Hours and Location

Before or after class, in the classroom

Contact

sanfo@em.u-hyogo.ac.jp

Corresponding Diploma Policy

A double circle indicates the most relevant DP number and a circle indicates the associated DP.

Corresponding Undergraduate School DP

1◎／2〇／3〇

Corresponding Graduate School DP

ー

Corresponding University-Wide DP

N/a

Academic Goals of Teacher Training Course

ー

Course Objectives and Learning Outcome

【Course Objectives】
This course is designed to equip students with the practical skills needed to apply econometric methods using R. The course will prepare students to analyze real-world data, extract meaningful insights, and effectively communicate results. Students will learn how to manipulate and visualize data, implement econometric models, and interpret the results. The course emphasizes a hands-on approach to ensure that students gain confidence in using R for data-driven decision-making in academic, research, and professional settings.

【Learning Outcome】
By the end of this course, students will be able to:
1. Utilize R to perform data cleaning, visualization, and basic econometric analysis.
2. Interpret and present analyzed results in a clear and actionable manner.
3. Carry out a basic empirical research project

Subtitle and Keywords of the Class

Econometric analysis; applied statistic; R; data analysis; data visualization

Course Overview and Schedule

This course builds on the hands-on and interactive approach of Seminar I, with increased emphasis on student independence and research initiative. Students are expected to take primary responsibility for defining their research topics, selecting appropriate data, and designing empirical analyses. The instructor’s role is to provide guidance, feedback, and methodological support throughout the research process rather than step-by-step instruction.

Active participation remains essential. The course is designed to foster autonomy, critical thinking, and confidence in conducting empirical research, creating a collaborative yet student-driven learning environment.

In-person/Remote Classification

Hybrid (In-person)

Implementation Method and Remote Credit Limit Application

Studentswill decide on a research topic. Then, they will conduct research on the chosen topic using real-world data. Econometric models will be implemented in R, but they can use other statistical packages.

For sessions conducted remotely, students must have the necessary equipment and internet access (e.g., a computer and a stable Wi-Fi connection) to attend classes from home or other locations. Sessions to be conducted remotely will be determined and communicated after course registration.

Uses of Generative AI

Limited permission for use

Precautions for using Generative AI

Students are required to comply with the University of Hyogo's policy regarding the use of generative AI tools. Generative AI may be used as a supplementary aid for learning activities such as drafting reports or conducting preliminary research, provided that students critically assess the content produced. Students are responsible for verifying the accuracy of all information, properly acknowledging sources and references, and ensuring that submitted work reflects their own understanding and independent effort. Assignments generated primarily by generative AI, or submitted without appropriate revision and original contribution, are not permitted.

If inappropriate use of generative AI is identified, the assignment may receive no credit or other academic measures may be taken in accordance with university regulations.

Textbook

Using R for Introductory Econometrics 2nd edition, by Florian Heiss
Can be accessed for free using this link: https://www.urfie.net/downloads/PDF/URfIE_web.pdf

References

Applied Statistics with R, by David Dalpiaz. https://book.stat420.org/
Introductory Econometrics: A Modern Approach, by Jeffrey M. Wooldridge
H. Stock & M. M. Watson Introduction to Econometrics 4th ed. Pearson

Contents and Estimated Time for Pre- and Post- Learning (Preparation and Review)

【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.

Contents of Active Learning

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.
Students will estimate econometric models and give presentations on their findings.

Grading Criteria and Methods

Engagement in classroom activities (40%)
Mid-term test (20%)
Research project presentation (40%)

How to Disclose Assignments and Exam Results

In the classroom

Precautions and Requirements for Course Registration

The prerequisite for this course is Introductory Statistics for Economics.

Students should have an understanding of statistics and basic econometrics.

Regular attendance is required.

Practical Education

N/A

Remarks

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!

In cases where any differences arise between the English version and the original Japanese version, the Japanese version shall prevail as the official authoritative version.