Prediction Using Several Macroeconomic Models

We establish methods that improve the predictions of macroeconometric modelsdynamic factor models, dynamic stochastic general equilibrium models, and vector autoregressionsusing a quarterly U.S. data set. We measure prediction quality with one-step-ahead probability densities assigned in real time. Two steps lead to substantial improvements: (a) the use of full Bayesian predictive distributions rather than conditioning on the posterior mode for parameters and (b) the use of an equally weighted pool.