TY  - BOOK
AU  - Freyberger,Joachim
AU  - Höppner,Björn
AU  - Neuhierl,Andreas
AU  - Weber,Michael
ED  - National Bureau of Economic Research.
TI  - Missing Data in Asset Pricing Panels
T2  - NBER working paper series
PY  - 2022///
CY  - Cambridge, Mass.
PB  - National Bureau of Economic Research
KW  - Semiparametric and Nonparametric Methods: General
KW  - jelc
KW  - Financial Econometrics
KW  - Asset Pricing • Trading Volume • Bond Interest Rates
N1  - December 2022; Hardcopy version available to institutional subscribers
N2  - 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 high-dimensional 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
UR  - https://www.nber.org/papers/w30761
UR  - http://dx.doi.org/10.3386/w30761
ER  -