  We develop a novel "decouple-recouple" dynamic predictive strategy and
contribute to the literature on forecasting and economic decision making in a
data-rich environment. Under this framework, clusters of predictors generate
different latent states in the form of predictive densities that are later
synthesized within an implied time-varying latent factor model. As a result,
the latent inter-dependencies across predictive densities and biases are
sequentially learned and corrected. Unlike sparse modeling and variable
selection procedures, we do not assume a priori that there is a given subset of
active predictors, which characterize the predictive density of a quantity of
interest. We test our procedure by investigating the predictive content of a
large set of financial ratios and macroeconomic variables on both the equity
premium across different industries and the inflation rate in the U.S., two
contexts of topical interest in finance and macroeconomics. We find that our
predictive synthesis framework generates both statistically and economically
significant out-of-sample benefits while maintaining interpretability of the
forecasting variables. In addition, the main empirical results highlight that
our proposed framework outperforms both LASSO-type shrinkage regressions,
factor based dimension reduction, sequential variable selection, and
equal-weighted linear pooling methodologies.
