This paper provides cross-country empirical evidence on term premia. I construct a panel of zero-coupon nominal government bond yields spanning ten industrialized countries and nearly two decades. I hence compute forward rates and use two different methods to decompose these forward rates into expected future short-term interest rates and term premiums. The first method uses an affine term structure model with macroeconomic variables as unspanned risk factors; the second method uses surveys. I find that term premiums declined internationally over the sample period, especially in countries that apparently reduced inflation uncertainty by making substantial changes in their monetary policy frameworks. (JEL E13, E43, E52, G12, H63)

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.