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.

中文翻译：

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资产定价面板中缺失数据

我们提出了一种简单且计算上有吸引力的方法，使用条件均值插补和加权最小二乘来处理横截面资产定价中的缺失数据，并采用广义矩法（GMM）框架。该方法允许我们使用所有具有观察到的回报的观察结果；它产生有效的推论；它可以应用于非线性和高维设置。在模拟中，我们发现它的性能几乎与高效但计算成本高的 GMM 估计器一样好。我们将我们的程序应用于一大组回报预测变量，发现它可以提高样本外的可预测性。