Missing Data in Asset Pricing Panels

• 成果类型: Article
• 署名作者: Freyberger, Joachim; Hoeppner, Bjoern; Neuhierl, Andreas; Weber, Michael
• 署名单位: University of Bonn; Washington University (WUSTL); University of Chicago
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhae003
• 发表日期: 2024
• 页码: 760
• 关键词: cross-section; generalized-method; sample properties; regression; returns; BIAS; INFORMATION; selection; models; tests

摘要

We propose a simple and computationally attractive method to deal with missing data in in cross-sectional asset pricing using conditional mean imputations and weighted least squares, cast in a generalized method of moments (GMM) framework. This method allows us to use all observations with observed returns; it results in valid inference; and it can be applied in nonlinear and high-dimensional settings. In simulations, we find it performs almost as well as the efficient but computationally costly GMM estimator. We apply our procedure to a large panel of return predictors and find that it leads to improved out-of-sample predictability.