美联储：线性状态空间模型中增强鲁棒滤波和预测的缺失数据替换（英文版）

Replacing faulty measurements with missing values can suppress outlier-induced distortions in state-space inference. We therefore put forward two complementary methods for enhanced outlier-robust filtering and forecasting: supervised missing data substitution (MD) upon exceeding a Huber threshold, and unsupervised missing data substitution via exogenous randomization (RMDX). Our supervised method, MD, is designed to improve performance of existing Huber-based linear filters known to lose opti