Through readings and discussions, the course introduces applications of secondary data and related research methodologies that complement analytical research in addressing business challenges. It covers basic concepts, common data structures, and empirical approaches, as well as the issues that these approaches tend to address. The course is for research-oriented graduate students, particularly suitable for students with basic knowledge of analytical modeling and an interest in the multi-method research approach.
Prerequisites: Students who have basic knowledge about analytical model and analysis, common optimization techniques, and linear regression. If you are unsure what to read in preparation for a specific topic, please feel free to ask the instructor.
Course Format
The class approaches this in three ways. First, the lecture will use a small portion of class time to cover basic knowledge. It is more for the purpose of review, as students are expected to finish the preassigned materials. The reading materials include textbook chapters and journal articles from top management journals. Second, students should read and synthesize articles in advance and present the outcomes in class. This activity is expected to be highly interactive, as the lectures and peers may discuss all possible aspects during the presentation. Third, students will be guided on the application of a variety of approaches in Python to analyze data in the project they proposed. Students will also practice research question formation and quantitative analysis with a topic and data of their choice to construct the methods and results portion of a research project targeting an academic journal. In sum, the course format is more similar to a research seminar with individual contributions rather than a lecture-based course.
Course Communication
You MUST have access to the course COOL website. The instructor will primarily use the website announcement function to communicate with the whole class. It is a student's responsibility to follow announcements and take action accordingly. Course contents are arranged by topics and organized using the modules function on the website so students can access materials easily. Course contents include lecture slides, journal articles, and Python code, which will all be uploaded to their corresponding modules. Again, it is the student's responsibility to retrieve them in advance. For individual matters, emailing the instructor directly is recommended. You are also welcome to use the message function in COOL, although it is hardly real-time.
Required Materials and/or Technology
Required Textbook
1
Wooldridge, J. M. (2016). Introductory Econometrics: A Modern Approach. 7th Edition. South-Western Cengage Learning.
2
Heiss, F., & Brunner, D. (2020). Using Python for Introductory Econometrics. 2nd Edition. Eigenverlag.
Optional Textbook
—
James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An Introduction to Statistical Learning: With Applications in Python. Springer International Publishing.
Software
Python (v3.12.5, conda-forge)
JupyterLab (v4.2.5)
Tentative Course Outline
Week
Date
Session
Topics or Activities
Format
Week 1
2/26
1
Introduction
In-person
Week 2
3/5
2
Why empirical analysis and secondary data?
In-person
Week 3
3/12
3
Basic methods
In-person
Week 4
3/19
4
Data structure
In-person
Week 5–9
3/26–4/23
—
Student project proposal
Remote
Week 10
4/30
5
Proxy
In-person
Week 11
5/7
6
Panel models
In-person
Week 12
5/14
7
Advance topic 1
In-person
Week 13
5/21
8
Advance topic 2
In-person
Week 14
5/28
9
Advance topic 3
In-person
Week 15
6/4
—
Presentation
In-person
Week 16
6/11
—
Presentation
In-person
Note: This is a tentative outline. A schedule table will be published on COOL and updated throughout the semester. Please refer to the table for assigned reading materials.
Grading Weight
Evaluation Area
Weight
Quiz
15%
Project report (presentation)
35%
Project report
25%
Class participation
25%
Total
100%
Student Project Proposal
Students will first come up with their proposal on how to apply the knowledge relevant to this course (empirical analysis) on a topic of their choice. The topic can be based on the student's personal interest, an ongoing research project, or an extension of a published analytical research article.
Project Presentation
Students will present their project in a 25-minute presentation. The activity is to practice students' presentation skills in a short time. The content will focus on topic introduction and approaches rather than findings — how you gain the interest of your class peers and demonstrate your competency in 25 minutes.
Participation and Attendance
During in-person sessions, quizzes will pop up randomly. Students' participation in classes is also considered and evaluated. Questioning and sharing thoughts are all considered and evaluated. For waiving attendance records, standard attendance policies are followed.
Score / Grade Appeals
It is important to recognize that a grade reflects others' judgment of your work. In this sense, all grading is subjective. Of course, any grade you receive is subject to appeal. However, score changes are at the discretion of the instructor and may be up or down based upon a complete review of the work in question. Changing a few points on an assignment rarely makes a difference in the final grade. Time is much better spent discussing and clarifying the content presented in the course.
