A new approach to multi-step forecasting using dynamic stochastic general equilibrium models

- We develop a method of estimating DSGE models using multi-step prediction errors.
- When applied to the benchmark Smets and Wouters model we get similar estimates to the original.
- Forecast performance is often improved, in several cases significantly.

DSGE models are of interest because they offer structural interpretations, but are also increasingly used for forecasting. Estimation often proceeds by methods which involve building the likelihood by one-step ahead (h=1) prediction errors. However in principle this can be done using different horizons where h>1. Using the well-known model of Smets and Wouters (2007), for h=1 classical ML parameter estimates are similar to those originally reported. As h extends some estimated parameters change, but not to an economically significant degree. Forecast performance is often improved, in several cases significantly.