EFFICIENT PREDICTION OF EXCESS RETURNS

• 成果类型: Article
• 署名作者: Faust, Jon; Wright, Jonathan H.
• 署名单位: Johns Hopkins University
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00092
• 发表日期: 2011-05
• 页码: 647-659
• 关键词: expected stock returns; dividend yields; interest-rates; bond; predictability; INFORMATION; inflation; inference; variables; MARKETS

摘要

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.