Missing Data in Asset Pricing Panels

doi:10.1093/rfs/hhae003

Missing Data in Asset Pricing Panels

2024-01-27

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DOI：10.1093/rfs/hhae003

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Abstract

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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.

Keywords:

C13

C58

G12

Missing Data in Asset Pricing Panels

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