Max Farrell studies econometric theory and applied econometrics. His research focuses on model selection, high-dimensional data, and robust semiparametric methods, with an emphasis on increasing reliability and implementability in data analysis. He is the co-author of “Optimal Convergence Rates, Bahadur Representation, and Asymptotic Normality of Partitioning Estimators” (Journal of Econometrics) and “Efficient Estimation of the Dose Response Function under Ignorability using Subclassification on the Covariates” (Advances in Econometrics).
Farrell earned a PhD in economics from the University of Michigan as well as an MA in statistics from the same institution. He pursued undergraduate studies at the Massachusetts Institute of Technology, where he earned SB degrees in both mathematics and economics.
Prior to his graduate studies, Farrell worked at the Center for Research on Health Care at the University of Pittsburgh and at Analysis Group, Inc, in support of a variety of statistical and economic consulting issues.
Farrell joined the University of Chicago faculty in 2014.