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Asymptotically Uniform Tests After Consistent Model Selection in the Linear Regression Model

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  • Adam McCloskey

Abstract

This article specializes the critical value (CV) methods that are based upon (refinements of) Bonferroni bounds, introduced by McCloskey to a problem of inference after consistent model selection in a general linear regression model. The post-selection problem is formulated to mimic common empirical practice and is applicable to both cross-sectional and time series contexts. We provide algorithms for constructing the CVs in this setting and establish uniform asymptotic size results for the resulting tests. The practical implementation of the CVs is illustrated in an empirical application to the effect of classroom size on test scores.

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  • Adam McCloskey, 2020. "Asymptotically Uniform Tests After Consistent Model Selection in the Linear Regression Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 810-825, October.
    • Handle: RePEc:taf:jnlbes:v:38:y:2020:i:4:p:810-825
    DOI: 10.1080/07350015.2019.1592754
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    Cited by:

    1. Doko Tchatoka, Firmin & Wang, Wenjie, 2021. "Size-corrected Bootstrap Test after Pretesting for Exogeneity with Heteroskedastic or Clustered Data," MPRA Paper 110899, University Library of Munich, Germany.
    2. Timothy B. Armstrong & Michal Kolesár, 2021. "Sensitivity analysis using approximate moment condition models," Quantitative Economics, Econometric Society, vol. 12(1), pages 77-108, January.
    3. Philipp Ketz & Adam McCloskey, 2021. "Short and Simple Confidence Intervals when the Directions of Some Effects are Known," Papers 2109.08222, arXiv.org.
    4. Doko Tchatoka, Firmin & Wang, Wenjie, 2021. "Uniform Inference after Pretesting for Exogeneity with Heteroskedastic Data," MPRA Paper 106408, University Library of Munich, Germany.

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