Generalized Shrinkage Methods for Forecasting Using Many Predictors
提出一种简单的收缩表示，描述多种针对大量正交预测变量（如主成分）的预测方法的操作特征，并用美国宏观经济数据（143个季度变量，1960-2008）实证比较这些方法与动态因子模型的预测效果。
This article provides a simple shrinkage representation that describes the operational characteristics of various forecasting methods designed for a large number of orthogonal predictors (such as principal components). These methods include pretest methods, Bayesian model averaging, empirical Bayes, and bagging. We compare empirically forecasts from these methods with dynamic factor model (DFM) forecasts using a U.S. macroeconomic dataset with 143 quarterly variables spanning 1960--2008. For most series, including measures of real economic activity, the shrinkage forecasts are inferior to the DFM forecasts. This article has online supplementary material.