Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance

By Marco Del Negro, Raiden Hasegawa and Frank Schorfheide


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 DSGE models with and without financial frictions for output growth and inflation from 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 forecasts in periods of financial distress but does not perform as well in tranquil periods. The dynamic pool’s weights react in a timely fashion to changes in the environment, leading to real-time forecast improvements relative to other methods of density forecast combination, such as Bayesian Model Averaging, optimal (static) pools, and equal weights. We show how a policymaker dealing with model uncertainty could have used a dynamic pools to perform a counterfactual exercise (responding to the gap in labor market conditions) in the immediate aftermath of the Lehman crisis.

Much work has be done in recent years to estimate DSGE model for forecasting purposes. Despite the fact that they are designed for these purposes, they turn out to be surprisingly useful. This paper continues in this line of research and shows that in fact DSGE models are most useful when other model are most likely to fail: when things are out of the ordinary. In hindsight this makes absolute sense. This is when you get out of the comfort zone of small fluctuations around a trend and theory can help you determine how agent react to event that are out of the sample. It turns out that instead of ditching DSGE models during the last crisis, as many have advocated, we should have used them even more than usual!


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