TY  - JOUR
AU  - Faust,Jon
AU  - Wright,Jonathan H.
TI  - Efficient Prediction of Excess Returns
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 14169
PY  - 2008
Y2  - July 2008
UR  - http://www.nber.org/papers/w14169
L1  - http://www.nber.org/papers/w14169.pdf
N1  - Author contact info:
Jon Faust
Johns Hopkins University
Department of Economics
Mergenthaler Hall 456
3400 N. Charles Street
Baltimore, MD 21218
Tel: 410/516-7614
Fax: 410/516-7600
E-Mail: faustj@jhu.edu

Jonathan H. Wright
Department of Economics
Johns Hopkins University
3400 N. Charles Street
Baltimore, MD 21218
Tel: 410/516-5728
Fax: 410/516-7600
E-Mail: wrightj@jhu.edu
AB  - 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.
ER  -