DSGE Model-Based Forecasting

TL;DR: In this article, the authors provide a real-time assessment of the forecasting performance of the Smets and Wouters (2007) model data up to 2011, compare it with Blue Chip and Greenbook forecasts, and show how it changes as they augment the standard set of observables with external information from surveys.

Abstract: Dynamic stochastic general equilibrium (DSGE) models use modern macroeconomic theory to explain and predict comovements of aggregate time series over the business cycle and to perform policy analysis. We explain how to use DSGE models for all three purposes – forecasting, story-telling, and policy experiments – and review their forecasting record. We also provide our own real-time assessment of the forecasting performance of the Smets and Wouters (2007) model data up to 2011, compare it with Blue Chip and Greenbook forecasts, and show how it changes as we augment the standard set of observables with external information from surveys (nowcasts, interest rates, and long-run inflation and output growth expectations). We explore methods of generating forecasts in the presence of a zero-lower-bound constraint on nominal interest rates and conditional on counterfactual interest rate paths. Finally, we perform a post-mortem of DSGE model forecasts of the Great Recession, and show that forecasts from a version of the Smets–Wouters model augmented by financial frictions and with interest rate spreads as an observable compare well with Blue Chip forecasts.