Exploiting the monthly data flow in structural forecasting

Domenico Giannone, Francesca Monti and Lucrezia Reichlin

No 751, Staff Reports from Federal Reserve Bank of New York

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.

Keywords: mixed-frequency data; temporal aggregation; large datasets; DSGE models; forecasting (search for similar items in EconPapers)
JEL-codes: C33 C53 E30 (search for similar items in EconPapers)
Pages: 45 pages
Date: 2015-12-01
New Economics Papers: this item is included in nep-dge, nep-ets, nep-for, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Exploiting the monthly data flow in structural forecasting (2016)
Working Paper: Exploiting the monthly data flow in structural forecasting (2014)
Working Paper: Exploiting the monthly data-flow in structural forecasting (2014)
Working Paper: Exploiting the monthly data-flow in structural forecasting (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fednsr:751

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