Optimal Cross-Sectional Regression

63 Pages Posted: 28 Jan 2021 Last revised: 25 Oct 2021

See all articles by Zhipeng Liao

Zhipeng Liao

University of California, Los Angeles (UCLA) - Department of Economics

Yan Liu

Purdue University

Date Written: September 30, 2021

Abstract

Errors-in-variables (EIV) biases plague asset pricing tests. We offer a new perspective on ad-
dressing 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.

Keywords: Beta uncertainty, Efficient esetimation, Factor models, Fama-MacBeth, GMM, Idiosyncratic risk, Systematic risk, Two-pass regression, Errors-in-variables

JEL Classification: C14, C22, G12

Suggested Citation

Liao, Zhipeng and Liu, Yan, Optimal Cross-Sectional Regression (September 30, 2021). Available at SSRN: https://ssrn.com/abstract=3719299 or http://dx.doi.org/10.2139/ssrn.3719299

Zhipeng Liao

University of California, Los Angeles (UCLA) - Department of Economics ( email )

8283 Bunche Hall
Los Angeles, CA 90095-1477
United States

Yan Liu (Contact Author)

Purdue University ( email )

West Lafayette, IN 47907-1310
United States

HOME PAGE: http://yliu1.com

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