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

At UW-Madison

PhD Level:

Econometrics (1st year): 4.67, 4.45, 4.61

• Topics: M-estimation: consistency and asymptotic normality, MLE, GMM, kernel density estimation, nonparametric regression estimation, bias-variance trade-off, curse of dimensionality, multinomial choice, selection bias, two-step estimation, semiparametric estimation and double machine learning

Econometric Methods (2nd year): 4.8, 4.8, 4.75

• Topics: empirical processes and double machine learning, exponential & maximal inequalities, metric entropies, Vapnik–Chervonenkis dimensions, U-statistics, jackknife, subsampling, Hoeffding decomposition, small bandwidth asymptotics, sparse networks, asymptotic distributional theory for 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 with AI, and Machine Learning: 4.3, 4.42, 4.07, 4.2

• Topics: bias-variance tradeoff, regression, classification, nearest neighbour algorithm, cross-validation, model selection, regularised estimation, bagging & random forests, deep neural networks, convolutional and recurrent neural networks, text embeddings, attention, transformers, large language models

*Two influential teachers of mine: Prof Werner Baer (1931-2016) and Prof Fred Gottheil (1931-2016). Photoed in 2014.