Missing Data in Asset Pricing Panels
提出一种基于条件均值插补和加权最小二乘的简单方法，在广义矩估计框架下处理资产定价中的缺失数据，允许使用所有观测值，适用于非线性和高维场景，模拟显示性能接近高效但计算昂贵的GMM估计，应用于大面板预测变量时提升了样本外预测能力。
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.