Variable Selection in Cross‐Section Regressions: Comparisons and Extensions
研究了在横截面回归中控制假发现率的多重检验程序，用于变量选择，并与常见模型选择准则比较，发现只有多重检验程序能一致控制假发现率但功效略低，在经济增长实证中与PcGets/Autometrics结果相似。
Abstract 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.