在横断面收益可预测性中,大多数声称的统计结果可能是正确的

Andrew Y. Chen
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引用次数: 4

摘要

Harvey、Liu和Zhu(2016)“认为金融经济学中大多数声称的研究结果可能是错误的。”令人惊讶的是,他们的错误发现率(FDR)估计显示,大多数都是正确的。我通过开发非参数和半参数的FDR估计器来重新审视他们的结果,这些估计器考虑了出版偏差和经验相关性。这些估计器提供了简单的封闭形式表达式,并在从经验预测器返回的聚类自举模拟中可靠地产生了FDR的上界。将这些估计值应用于Chen-Zimmermann的205个预测因子的数据集,我发现在横断面可预测性文献中声称的大多数统计结果可能是正确的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Most Claimed Statistical Findings in Cross-Sectional Return Predictability Are Likely True
Harvey, Liu, and Zhu (2016) “argue that most claimed research findings in financial economics are likely false.” Surprisingly, their false discovery rate (FDR) estimates suggest most are true. I revisit their results by developing non- and semi-parametric FDR estimators that account for publication bias and empirical correlations. These estimators provide simple closed-form expressions and reliably produce an upper bound on the FDR in simulations that cluster-bootstrap from empirical predictor returns. Applying these estimators to the Chen-Zimmermann dataset of 205 predictors, I find that most claimed statistical findings in the cross-sectional predictability literature are likely true.
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