Factor-adjusted Regularized Model Selection
Team Information
Team Members
Kaizheng Wang, Assistant Professor, Industrial Engineering and Operations Research, School of Engineering and Applied Science, Columbia University
Abstract
We study model selection consistency for high dimensional sparse regression when the data exhibit both cross-sectional and serial dependency. Motivated by financial studies, we consider the case where covariate dependence can be reduced through the factor model, and propose a consistency strategy named Factor-Adjusted Regularized Model Selection (FARMSelect). We use the U.S. bond risk premium data to demonstrate the nice finite-sample performance in terms of both model selection and out-of-sample prediction.
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Team Contact: Kaizheng Wang (use form to send email)
