02602cam a22002777 4500001000700000003000500007005001700012008004100029100001600070245010000086260006600186490004200252500001500294520122200309530006101531538007201592538003601664690016901700690011001869690012701979700002402106710004202130830007702172856003802249856003702287w14169NBER20140916153109.0140916s2008 mau||||fs|||| 000 0 eng d1 aFaust, Jon.10aEfficient Prediction of Excess Returnsh[electronic resource] /cJon Faust, Jonathan H. Wright. aCambridge, Mass.bNational Bureau of Economic Researchc2008.1 aNBER working paper seriesvno. w14169 aJuly 2008.3 aIt 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. aHardcopy version available to institutional subscribers. aSystem requirements: Adobe [Acrobat] Reader required for PDF files. aMode of access: World Wide Web. 7aC22 - Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models &bull Diffusion Processes2Journal of Economic Literature class. 7aC53 - Forecasting and Prediction Methods • Simulation Methods2Journal of Economic Literature class. 7aG14 - Information and Market Efficiency • Event Studies • Insider Trading2Journal of Economic Literature class.1 aWright, Jonathan H.2 aNational Bureau of Economic Research. 0aWorking Paper Series (National Bureau of Economic Research)vno. w14169.4 uhttp://www.nber.org/papers/w1416941uhttp://dx.doi.org/10.3386/w14169