2023-Present
Course description: This course provides a rigorous foundation in advanced econometric methods essential for analyzing non-experimental microeconomic data. Designed for graduate students, the curriculum bridges theoretical econometrics and empirical research by emphasizing the identification and estimation of causal relationships. Students explore workhorse microeconometric methods, including M-estimation, IV and GMM, panel data analysis, quantile regression, and nonparametric estimation, as well as hypothesis testing and simulation-based methods. The syllabus also integrates modern big data and machine learning techniques tailored for econometric applications. By combining theoretical rigor with practical applications using statistical software, the course equips students to critically evaluate the contemporary empirical literature and conduct independent research.
Enrollment: 15-30
Teaching evaluation: 4.8/5 (2023), 4.8/5 (2024), 4.6/5 (2025).
Before 2025, this course was co-badged with UQ's economics honors econometrics course ECON6300.
2018-Present
Course description: This course provides a comprehensive introduction to the statistical and mathematical techniques required for rigorous empirical economic research. Designed as an introductory course to econometrics, the curriculum centers on the multiple linear regression model as the primary framework for analyzing economic data. Students explore the core theory of linear regression, including hypothesis testing and model specification, while systematically addressing common data challenges such as heteroscedasticity, autocorrelation, and endogeneity. Building on these foundations, the course introduces essential methods including instrumental variables, panel data regression, binary choice models, natural experiments, and time series analysis. By integrating theoretical rigor with applications using real-world data, students develop the proficiency needed to execute independent empirical projects, interpret complex statistical results, and critically evaluate the quantitative evidence presented in contemporary economic literature.
Enrollment: 250-300
Teaching evaluation: 4.3/5 (2023), 4.4/5 (2024), 4.3/5 (2025)
2020-2022
Course description: This course offers advanced undergraduate students rigorous training in econometric methods for analyzing individual-level data and uncovering causal relationships. Built on the Rubin causal model and the potential outcomes framework, the curriculum centers on regression-based techniques for cross-sectional and panel data. It emphasizes the critical distinction between correlation and causation and equips students to estimate, interpret, and assess empirical models. Students are introduced to workhorse empirical methods, including Inverse Probability Weighting (IPW), Two-Way Fixed Effects (TWFE), Instrumental Variables (IV), Difference-in-Differences (DiD), Regression Discontinuity Design (RDD), and Propensity Score Matching (PSM). Core content draws on real-world data and research from fields such as labor, health, and education, preparing students to engage with contemporary applied microeconomic research and develop their own empirical ideas. Successful completion enhances both practical data-analysis proficiency and the ability to critically evaluate applied econometric work published in leading academic journals.
Enrollment: 40-70
Teaching evaluation: 4.0/5 (2020), N/A (2021), 4.4/5 (2022)
2019: ECON3350/7350 Applied Econometrics for Macroeconomics and Finance
2019: ECON3370/7371 Econometrics of Panel Data and Discrete Variables
2017-2018: Financial Econometrics [2017, 2018] (Nankai University)
2016: ECON 208 Introduction to Econometrics [2016] (Duke University)