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A one covariate at a time, multiple testing approach to variable selection in high‐dimensional linear regression models: A replication in a narrow sense

Hector Nuñez () and Jesús Otero

Journal of Applied Econometrics, 2021, vol. 36, issue 6, 833-841

Abstract: Chudik, Kapetanios, & Pesaran (Econometrica 2018, 86, 1479‐1512) propose a one covariate at a time, multiple testing (OCMT) approach to variable selection in high‐dimensional linear regression models as an alternative approach to penalised regression. We offer a narrow replication of their key OCMT results based on the Stata software instead of the original MATLAB routines. Using the new user‐written Stata commands baing and ocmt, we find results that match closely those reported by these authors in their Monte Carlo simulations. In addition, we replicate exactly their findings in the empirical illustration, which relate to top five variables with highest inclusion frequencies based on the OCMT selection method.

Date: 2021
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