Quantitative Economics and Finance in Asia is an empirical, methods-first course on how to design, implement, and interpret causal evidence in the context of Asian economies and financial markets. Rather than surveying many tools or country cases, the course focuses on a small set of core empirical designs—descriptive measurement, input–output accounting, panel fixed effects, difference-in-differences and event studies, instrumental variables, and exposure-based designs—and studies them slowly and in depth.
Each week revolves around one dominant empirical design. Students learn how to move from an economic question to a clearly defined estimand, identify the source of variation, state the key identifying assumption, implement the design with transparent code, and interpret results honestly. The Asia focus—China, Japan, Korea, ASEAN, and India—serves as a unifying empirical environment rather than a sequence of policy case studies.
The course emphasizes replication, diagnostics, and disciplined interpretation. Students repeatedly work with a small number of reusable datasets and code templates, allowing them to internalize core methods rather than spend time on bespoke data construction. Written work takes the form of concise, reproducible memos that prioritize clarity of design and assumptions over technical sophistication.
By the end of the course, students are prepared to replicate and extend applied empirical papers, evaluate causal claims in policy and finance contexts, and communicate quantitative evidence clearly and credibly to both academic and non-academic audiences.
Learning Objectives
Use quantitative data to analyze key economic and financial issues in Asian economies.
Understand how economists identify causal relationships using modern empirical methods.
Apply core tools such as panel data analysis, difference-in-differences, event studies, and input–output analysis.
Evaluate the credibility of empirical evidence in academic and policy research
Replicate empirical studies and communicate results clearly using concise, data-driven memos
Expected Learning Outcomes
Analyze economic and financial developments in Asian economies using quantitative data.
Apply core empirical methods to evaluate causal relationships in economics and finance.
Interpret results from panel data, difference-in-differences, event studies, and input–output analyses.
Assess the credibility and limitations of empirical evidence in academic and policy contexts.
Replicate and communicate empirical findings clearly using concise, reproducible analyses.
Course Contents
Week 1 Introduction to empirical data analysis
Weeks 2–3 Ordinary Least Squares (OLS) and its limitations
Weeks 4–6 Panel data and quasi-experimental designs
Weeks 7–8 Instrumental variables
Weeks 9–10 Networks and economic interactions
Weeks 11–12 Student presentations: empirical work or paper discussions
Weeks 13–14 Discrete choice models
Weeks 15–16 Group project presentations
Week 17 Concluding discussion
Assessment Elements
Attendance
Students individual presentations
Weekly presentations pairs
End of class problems
Final group project
Interim Assessment
2025/2026 4th module
0.25 * Students individual presentations + 0.1 * Attendance + 0.25 * Weekly presentations pairs + 0.15 * End of class problems + 0.25 * Final group project
Bibliography
Recommended Core Bibliography
Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics : An Empiricist’s Companion. Princeton: Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=329761
Introductory econometrics: a modern approach, Wooldridge, J. M., 2016
Recommended Additional Bibliography
Jeffrey M. Wooldridge. (2019). Introductory Econometrics: A Modern Approach, Edition 7. Cengage Learning.
Преподаватель
Десятников Иван Васильевич
Course Syllabus
Abstract
Learning Objectives
Expected Learning Outcomes
Course Contents
Assessment Elements
Interim Assessment
Bibliography
Recommended Core Bibliography
Recommended Additional Bibliography
Authors