Optimal Cross-Sectional Regression
针对资产定价测试中的变量误差偏差，提出将偏差视为具有特定相关结构的收益创新，并据此设计新回归模型，以更准确地估计风险溢价。
Errors-in-variables (EIV) biases plague asset pricing tests. We offer a new perspective on addressing the EIV issue: instead of viewing EIV biases as estimation errors that potentially contaminate next stage risk premium estimates, we consider them to be return innovations that follow a particular correlation structure. We factor this structure into our test design, yielding a new regression model that generates the most accurate risk premium estimates. We demonstrate the theoretical appeal as well as the empirical relevance of our new estimator. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The supplemental appendix and data files are available at https://doi.org/10.1287/mnsc.2023.4966 .