Post-Selection Inference for Generalized Linear Models with Many Controls

Alexandre Belloni, Victor Chernozhukov and Ying Wei

Papers from arXiv.org

Abstract: This paper considers generalized linear models in the presence of many controls. We lay out a general methodology to estimate an effect of interest based on the construction of an instrument that immunize against model selection mistakes and apply it to the case of logistic binary choice model. More specifically we propose new methods for estimating and constructing confidence regions for a regression parameter of primary interest $\alpha_0$, a parameter in front of the regressor of interest, such as the treatment variable or a policy variable. These methods allow to estimate $\alpha_0$ at the root-$n$ rate when the total number $p$ of other regressors, called controls, potentially exceed the sample size $n$ using sparsity assumptions. The sparsity assumption means that there is a subset of $s

Date: 2013-04, Revised 2016-03
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Citations: View citations in EconPapers (55)

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Journal Article: Post-Selection Inference for Generalized Linear Models With Many Controls (2016)
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