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.