Inference after lasso model selection

David Drukker

2019 Stata Conference from Stata Users Group

Abstract: The increasing availability of high-dimensional data and increasing interest in more realistic functional forms have sparked a renewed interest in automated methods for selecting the covariates to include in a model. I discuss the promises and perils of model selection and pay special attention to estimators that provide reliable inference after model selection. I will demonstrate how to use Stata 16's new features for double selection, partialing out, and cross-fit partialing out to estimate the effects of variables of interest while using lasso methods to select control variables.

Date: 2019-08-02
New Economics Papers: this item is included in nep-big
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Citations: View citations in EconPapers (2)

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http://fmwww.bc.edu/repec/scon2019/chicago19_Drukker.pdf

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Working Paper: Inference after lasso model selection (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:boc:scon19:3

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