Missing Data in Asset Pricing Panels

Joachim Freyberger, Bjoern Hoeppner, Andreas Neuhierl and Michael Weber

The Review of Financial Studies, 2025, vol. 38, issue 3, 760-802

Abstract: 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.

JEL-codes: C13 C58 G12 (search for similar items in EconPapers)
Date: 2025
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Working Paper: Missing Data in Asset Pricing Panels (2022)
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