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Exploiting the monthly data flow in structural forecasting
Domenico Giannone
Francesca Monti
Lucrezia Reichlin
Abstract

This paper develops a framework that allows us to combine the tools provided by structural models for economic interpretation and policy analysis with those of reduced-form models designed for nowcasting. We show how to map a quarterly dynamic stochastic general equilibrium (DSGE) model into a higher frequency (monthly) version that maintains the same economic restrictions. Moreover, we show how to augment the monthly DSGE with auxiliary data that can enhance the analysis and the predictive accuracy in now-casting and forecasting. Our empirical results show that both the monthly version of the DSGE and the auxiliary variables offer help in real time for identifying the drivers of the dynamics of the economy.


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Domenico Giannone & Francesca Monti & Lucrezia Reichlin, Exploiting the monthly data flow in structural forecasting, Federal Reserve Bank of New York, Staff Reports 751, 01 Dec 2015.
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Keywords: DSGE models; forecasting; temporal aggregation; mixed-frequency data; large data sets
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