Christian B. Hansen, Ph.D.
Wallace W. Booth Professor of Econometrics and Statistics
The University of Chicago Booth School of Business
ABSTRACT: This paper proposes a post-model selection inference procedure, called targeted undersmoothing, designed to construct uniformly valid confidence sets for functionals of sparse high-dimensional models, including dense functionals that may depend on many or all elements of the high-dimensional parameter vector. The confidence sets are based on an initially selected model and two additional models which enlarge the initial model. We apply the procedure in two empirical examples: estimating heterogeneous treatment effects in a job training program and estimating profitability from an estimated mailing strategy in a marketing campaign. We also illustrate the procedure’s performance through simulation experiments.
BIO: Christian B. Hansen studies applied and theoretical econometrics, the uses of high-dimensional statistical methods in economic applications, estimation of panel data models, quantile regression, and weak instruments. In 2008, Hansen was named a Neubauer Family Faculty Fellow, and he was named the Wallace W. Booth professorship in 2014. Hansen’s recent research has focused on the uses of high-dimensional data and methods in economics applications. The papers “Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain” with A. Belloni, D. Chen, and V. Chernzhukov (Econometrica, 2012) and “Inference on Treatment Effects after Selection amongst High-Dimensional Controls” with A. Belloni and V. Chernozhukov (Review of Economic Studies, 2014) present approaches to estimating structural or treatment effects from economic data in canonical instrumental variables and treatment effects models. These papers are extended in “Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach” with V. Chernozhukov and M. Spindler (Annual Review of Economics, 2015) and the forthcoming papers “Inference in High Dimensional Panel Models with an Application to Gun Control” with A. Belloni, V. Chernozhukov, and D. Kozbur (Journal of Business and Economic Statistics) and “Program Evaluation with High-Dimensional Data” with A. Belloni, V. Chernozhukov, and I. Fernández-Val (Econometrica).
Hansen has published articles regarding identification and estimation in panel data models, inference with data that may be spatially and temporally dependent, quantile regression, and instrumental variables models with weak or many instruments. His published work has appeared in several journals including Econometrica, the Journal of Business and Economic Statistics, the Journal of Econometrics, and the Review of Economics and Statistics. He graduated from Brigham Young University with a bachelor’s degree in economics in 2000. In 2004, he received a PhD in economics from the Massachusetts Institute of Technology, where he was a graduate research fellow of the National Science Foundation. He joined the Chicago Booth faculty in 2004.