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Exploiting the monthly data flow in structural forecasting

Domenico Giannone, Francesca Monti and Lucrezia Reichlin

No 509, Bank of England working papers from Bank of England

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 (search for similar items in EconPapers)
JEL-codes: C33 C53 E30 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2014-09-12
New Economics Papers: this item is included in nep-dge, nep-ets, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Exploiting the monthly data flow in structural forecasting (2016) Downloads
Working Paper: Exploiting the monthly data flow in structural forecasting (2015) Downloads
Working Paper: Exploiting the monthly data-flow in structural forecasting (2014) Downloads
Working Paper: Exploiting the monthly data-flow in structural forecasting (2014) Downloads
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