Optimal Cross-Sectional Regression

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 .

• 저널Management Science · 70(11) · 7911–7942
• 토픽Financial Risk and Volatility Modeling · Finance
• DOI10.1287/mnsc.2023.4966
• 저자Zhipeng Liao, Yan Liu, Zhenzhen Xie