  This paper is concerned with detecting the presence of out of sample
predictability in linear predictive regressions with a potentially large set of
candidate predictors. We propose a procedure based on out of sample MSE
comparisons that is implemented in a pairwise manner using one predictor at a
time and resulting in an aggregate test statistic that is standard normally
distributed under the global null hypothesis of no linear predictability.
Predictors can be highly persistent, purely stationary or a combination of
both. Upon rejection of the null hypothesis we subsequently introduce a
predictor screening procedure designed to identify the most active predictors.
An empirical application to key predictors of US economic activity illustrates
the usefulness of our methods and highlights the important forward looking role
played by the series of manufacturing new orders.
