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We provide a novel methodology for estimating time-varying weights in linear prediction pools, which we call dynamic pools, and use it to investigate the relative forecasting performance of dynamic stochastic general equilibrium (DSGE) models, with and without financial frictions, for output growth and inflation in the period 1992 to 2011. We find strong evidence of time variation in the pool's weights, reflecting the fact that the DSGE model with financial frictions produces superior forecastsdoi:10.2139/ssrn.2506037 fatcat:iwzqlmuogrf6zgzzifwioftcoy