Optimal Cross-Sectional Regression

• 成果类型: Article
• 署名作者: Liao, Zhipeng; Liu, Yan; Xie, Zhenzhen
• 署名单位: University of California System; University of California Los Angeles; Tsinghua University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.4966
• 发表日期: 2024
• 关键词: beta uncertainty; efficient estimation; errors in variables; factor models; Fama-MacBeth; gmm; idiosyncratic risk; systematic risk; two-pass regression

摘要

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.