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Inference after lasso model selection

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  • David Drukker

    (StataCorp)

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," 2019 Stata Conference 3, Stata Users Group.
    • Handle: RePEc:boc:scon19:3
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    File URL: http://fmwww.bc.edu/repec/scon2019/chicago19_Drukker.pdf
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    References listed on IDEAS

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    1. Pötscher, Benedikt M. & Leeb, Hannes, 2009. "On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2065-2082, October.
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    Cited by:

    1. Luigi Guiso & Alexey Makarin, 2020. "Affinity, Trust, and Information," EIEF Working Papers Series 2020, Einaudi Institute for Economics and Finance (EIEF), revised Sep 2020.

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