Survey_DNS_2013

Abstract

On the methodological side, we provide a collection of algorithms that can be used to generate forecasts with DSGE models that have been estimated with Bayesian methods. In particular, we focus on novel methods that allow the user to incorporate external information into the DSGE-model-based forecasts. This external information could take the form of forecasts for the current quarter (nowcasts) from surveys of professional forecasters, short-term and medium-term interest rate forecasts, or long-run inflation and output-growth expectations. On the substantive side, we are providing detailed empirical applications of the forecasting methods. The empirical analysis features small and medium-scale DSGE models estimated on U.S. data. The novel aspects of the empirical analysis are to document how the forecast performance of the Smets and Wouters (2007) model can be improved by incorporating data on long-run inflation expectations as well as nowcasts from the Blue Chip Survey. We also show that data on short- and medium-horizon interest rate expectations improves the interest rate forecasts of the Smets-Wouters model with anticipated monetary policy shocks, but has some adverse effects on output growth and inflation forecasts. Finally, we provide new insights in the real-time forecasting performance of the Smets-Wouters model and a DSGE model with financial frictions during the 2008-09 recession.