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Exploiting the monthly data flow in structural forecasting
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DOI：10.1016/j.jmoneco.2016.10.011.png)
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Abstract
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A quarterly stochastic general equilibrium (DSGE) model is combined with a now-casting model designed to read timely monthly information as it becomes available. This implies (1) mapping the structural quarterly DSGE with a monthly version that maintains the same economic restrictions; (2) augmenting the model with a richer data set and (3) updating the estimates of the DSGE's structural shocks in real time following the publication calendar of the data. Our empirical results show that our methodology enhances the predictive accuracy in now-casting. An analysis of the Great Recession also shows that our framework would have helped tracing the DSGE's structural shocks in real time, obtaining, for example, a more timely account of the 2008 contraction. Crown Copyright (C) 2016 Published by Elsevier B.V. All rights reserved.
Keywords:
DSGE models
Forecasting
Temporal aggregation
Mixed frequency data
Large datasets
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