Teaching

Directed Study: Spring 2024

Directed study on advanced topics in econometrics/statistical learning theory: Concentration Inequalities, Uniform Law of Large Numbers, Sparse linear model in high-dimensions, Reproducing Kernel Hilbert Spaces, Semiparametric Efficiency Theory in Causal Inference, and Double/Debiased Machine Learning.

Teaching Assistant: Spring 2024

This is a required course in the Econometrics PhD core sequence.

Teaching Assistant: Fall 2023, Fall 2024

This class is to introduce fundamental ideas in statistical inference. It includes probability theory, and traditional parametric statistical inference, estimation, and hypothesis testing.

Teaching Assistant: Fall 2023

Introduction to construction and testing of econometric models; analysis and critique of general linear regression model; simultaneous equations models; computer program for regression analysis; applications.

Teaching Assistant: Fall 2018, Spring 2018, Fall 2019.

Undergraduate-level introduction to causal inference: Potential Outcomes Framework, Instrumental Variables, Regression Discontinuity Design, Panel Data and Difference-in-Differences.

Teaching Assistant: Spring 2017.

Undergraduate-level introduction to statistical inference: Continuous Distributions, Point Estimate, Confidence Interval, Hypothesis Testing, Maximum Likelihood Estimation, ANOVA, and Linear Regression.