Abstract The reliability of tests on the risk premia in linear factor models is threatened by limited sample sizes and weak identification of risk premia frequently encountered in applied work. We, therefore, propose novel tests on the risk premia that are robust to both limited sample sizes and the identification strength of the risk premia as reflected by the quality of the risk factors. These tests are appealing for empirically relevant settings, and lead to confidence sets of risk premia that can substantially differ from conventional ones. To show the latter, we revisit two high-profile empirical applications.

中文翻译：

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有限样本中风险溢价的识别稳健测试

摘要 线性因子模型中风险溢价检验的可靠性受到样本量有限和应用工作中经常遇到的风险溢价识别薄弱的威胁。因此，我们提出了关于风险溢价的新测试，这些测试对于有限的样本量和风险因素的质量所反映的风险溢价的识别强度都是稳健的。这些测试对经验相关的设置很有吸引力，并导致风险溢价的置信度集与传统的风险溢价有很大不同。为了展示后者，我们重新审视了两个备受瞩目的实证应用。