Identification Robust Testing of Risk Premia in Finite Samples
针对线性因子模型中风险溢价检验受限于小样本和弱识别的问题，提出了对样本量和识别强度都稳健的新检验方法，并重新审视了两个知名实证案例。
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.