Courses I have taught and their evaluations (out of 5):

At UW-Madison

PhD Level:

Econometrics (1st year): 4.45, 4.61

• Topics: M-estimation, MLE, GMM, limited dependent variables, selection bias, two-step estimation, nonparametric estimation, regression discontinuity designs, semiparametric estimation

Econometric Methods (2nd year): 4.8, 4.75

• Topics: double/debiased machine learning, exponential & maximal inequalities, metric entropies, VC dimensions, U-statistics, jackknife, subsampling, Hoeffding decomposition, small bandwidth asymptotics, sparse networks, asymptotics for (honest) random forests

MS Level:

Econometrics I:  4.5, 4.4, 4.11

• Topics: causal inference, randomised controlled trials, unconfoundedness, matching, inverse probability weighting, doubly robust estimation, nonparametric estimation, regression discontinuity designs

Econometrics II: 4.38, 4.15, 4.02

• Topics: cluster robust inference, resampling methods, random forests, high-dimensional regression models,  double/debiased machine learning, 

Undergraduate Level:

Econometrics for Big Data: 4.42, 4.07, 4.2

• Topics: statistical learning, basics of R-programming, regression, classification, nearest neighbour algorithm, cross-validation, linear model selection, regularised estimation, bagging & random forests