Exploiting the Monthly Data Flow in Structural Forecasting

Abstract

This paper shows how and when it is possible to obtain a mapping from a quarterly dynamic stochastic general equilibrium (DSGE) model to a monthly specification that maintains the same economic restrictions and has real coefficients. We use this technique to derive the monthly counterpart of the well-known DSGE model by Galí, Smets and Wouters (GSW) for the US economy. We then augment it with auxiliary macro indicators which, because of their timeliness, can be used to obtain a nowcast of the structural model. We show empirical results for the quarterly growth rate of GDP, the monthly unemployment rate and GSW’s welfare-relevant output gap. Results show that the augmented monthly model does best for nowcasting.

Keywords: Forecasting, temporal aggregation, mixed frequency data, large data sets

JEL Classification: C33, C53, E30