Faculty & Research

Max Farrell

Assistant Professor of Econometrics and Statistics, and John E. Jeuck Faculty Fellow

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5807 South Woodlawn Avenue
Chicago, IL 60637

Max H. Farrell studies econometric theory and applied econometrics. His research focus on model selection, high-dimensional data, and robust semiparametric methods, with a focus on increasing reliability and implementability in data analysis. His publications appear in the Journal of Econometrics and Advances in Econometrics, as well as a variety of healthcare and medical journals.

Farrell earned a Ph.D. in economics from the University of Michigan, as well as an M.A. in statistics. Farrell pursued undergraduate studies at the Massachusetts Institute of Technology where he earned S.B. degrees in mathematics and economics. He has experience teaching statistics and econometrics at the undergraduate and graduate level.

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, where he worked on a variety of statistical and economic consulting issues.


2016 - 2017 Course Schedule

Number Name Quarter
41100 Applied Regression Analysis 2016 (Fall)

REVISION: Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations
Date Posted: Apr  03, 2015
This paper concerns robust inference on average treatment effects following model selection. In the selection on observables framework, we show how to construct confidence intervals based on a doubly-robust estimator that are robust to model selection errors and prove that they are valid uniformly over a large class of treatment effect models. The class allows for multivalued treatments with heterogeneous effects (in observables), general heteroskedasticity, and selection amongst (possibly) more covariates than observations. Our estimator attains the semiparametric efficiency bound under appropriate conditions. Precise conditions are given for any model selector to yield these results, and we show how to combine data-driven selection with economic theory. For implementation, we give a specific proposal for selection based on the group lasso and derive new technical results for high-dimensional, sparse multinomial logistic regression. A simulation study shows our estimator performs ...