Year: 2022
Type: report
Abstract: Missing data for return predictors is a common problem in cross sectional asset pricing.Most papers do not explicitly discuss how they deal with missing data but conventional treatments focus on the subset of firms with no missing data for any predictor or impute the unconditional mean.Both methods have undesirable properties -they are either inefficient or lead to biased estimators and incorrect inference.We propose a simple and computationally attractive alternative 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 non-linear and highdimensional settings.In Monte Carlo simulations, we find that it performs almost as well as the efficient but computationally costly GMM estimator in many cases.We apply our procedure to a large panel of return predictors and find that it leads to improved out-of-sample predictability.
DOI: 10.3386/w30761
Institutions: National Bureau of Economic Research, University of Bonn, University of Mannheim, University of Washington, Washington University in St. Louis, Yale University, University of Chicago, University of Virginia, The Ohio State University, Rice University, Temple University, University of Oklahoma
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4311940029