Efficient Prediction of Excess Returns
提出在预测超额收益时，通过加入与误差项相关但与原变量无关的变量（如调查预测误差、新闻冲击等）来提高估计效率和预测精度，并应用于债券和股票超额收益预测。
It is well known that augmenting a standard linear regression model with variables that are correlated with the error term but uncorrelated with the original regressors will increase the asymptotic efficiency of the original coefficients. We argue that in the context of predicting excess returns, valid augmenting variables exist and are likely to yield substantial gains in estimation efficiency and, hence, predictive accuracy. The proposed augmenting variables are ex post measures of an unforecastable component of excess returns: ex post errors from macroeconomic survey forecasts, the surprise components of asset price movements around macroeconomic news announcements, or even the weather. These "“surprises"” cannot be used directly in forecasting-—they are not observed at the time that the forecast is made-—but can nonetheless improve forecasting accuracy by reducing parameter estimation uncertainty. We derive formal results about the benefits and limits of this approach and apply it to standard examples of forecasting excess bond and equity returns. We find substantial improvements in out-of-sample forecast accuracy for standard excess bond return regressions; gains for forecasting excess stock returns are much smaller. © 2011 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.