Efficient prediction of excess returns
By: Jon Faust, Jonathan H. Wright.
Contributors: Wright, Jonathan H.; National Bureau of Economic Research.
Series: NBER working paper series -- working paper 14169; Working paper series (National Bureau of Economic Research : Online) -- working paper no. 14169.
Publisher: National Bureau of Economic Research
Published: 2008
Place of publication: Cambridge, MA
Language: eng
Format: Electronic resource
Description: "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 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 and the surprise components of asset price movements around macroeconomic news announcements.  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"--National Bureau of Economic Research web site.
Notes: Title from PDF file as viewed on 7/23/2008.

Includes bibliographical references.

Also available in print.

System requirements: Adobe Acrobat Reader.

Mode of access: World Wide Web.