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Inference after lasso model selection

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  • David Drukker

    (StataCorp LP)

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.

Suggested Citation

  • David Drukker, 2019. "Inference after lasso model selection," London Stata Conference 2019 25, Stata Users Group.
    • Handle: RePEc:boc:usug19:25
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    File URL: http://repec.org/usug2019/Drukker_uk19.pdf
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    Cited by:

    1. Makarin, Alexey & Guiso, Luigi, 2020. "Affinity, Trust, and Information," CEPR Discussion Papers 15250, C.E.P.R. Discussion Papers.

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