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Variable Selection in Cross‐Section Regressions: Comparisons and Extensions
Author(s) -
Deckers Thomas,
Hanck Christoph
Publication year - 2014
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/obes.12048
Subject(s) - false discovery rate , selection (genetic algorithm) , model selection , bayesian probability , econometrics , statistics , feature selection , multiple comparisons problem , section (typography) , mathematics , computer science , machine learning , biochemistry , chemistry , gene , operating system
Cross‐section regressions often examine many candidate regressors. We use multiple testing procedures (MTPs) controlling the false discovery rate (FDR) — the expected ratio of false to all rejections — so as not to erroneously select variables because many tests were performed, yielding a simple model selection procedure. Simulations comparing the MTPs with other common model selection criteria demonstrate that, for conventional tuning parameters of the selection procedures, only MTPs consistently control the FDR, but have slightly lower power. In an empirical application to growth, MTPs and PcGets/Autometrics identify similar growth determinants, which differ somewhat from those obtained by Bayesian Model Averaging.