A shrinkage estimator that accounts for estimation risks is developed and employed to re-access whether the monetary fundamentals help predict changes in exchange rates. The suggested estimator is featured by optimally pooling information from cross-sections across time and that from the individual time series of concern. While the estimation risk in term of mean squared errors takes place in the presence of param- eter uncertainties, it is more problematic in the context of the exchange rate predictive regression where the time series under study are typically short and the predictors are highly persistent. Monte-Carlo simulations clearly demonstrate that comparing to the least-square estimator, the magnitude of estimation errors associated with our shrink- age estimator is 10% to 35% less. Moreover, the risk reductions convert into sizable power gains for tests for predictability, yielding a robust inference. In contrast to the previous studies, a uniform evidence of the higher ability of monetary fundamentals to forecast exchange rate movements is now found at both short and long horizons, whether in-sample or out-of-sample.