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

DOI

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.

Identifier
DOI https://doi.org/10.15456/jae.2022327.0719778190
Metadata Access https://www.da-ra.de/oaip/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:oai.da-ra.de:775195
Provenance
Creator Nuñez, Hector M.; Otero, Jesús
Publisher ZBW - Leibniz Informationszentrum Wirtschaft
Publication Year 2021
Rights Creative Commons Attribution 4.0 (CC-BY); Download
OpenAccess true
Contact ZBW - Leibniz Informationszentrum Wirtschaft
Representation
Language English
Resource Type Collection
Discipline Economics