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Nowcasting in Real Time Using Popularity Priors

George Monokroussos

MPRA Paper from University Library of Munich, Germany

Abstract: This paper proposes a Bayesian nowcasting approach that utilizes information coming both from large real-time data sets and from priors constructed using internet search popularity measures. Exploiting rich information sets has been shown to deliver significant gains in nowcasting contexts, whereas popularity priors can lead to better nowcasts in the face of model and data uncertainty in real time, challenges which can be particularly relevant during turning points. It is shown, for a period centered on the latest recession in the United States, that this approach has the potential to deliver particularly good real-time nowcasts of GDP growth.

Keywords: Nowcasting; Gibbs Sampling; Factor Models; Kalman Filter; Real-Time Data; Google Trends; Monetary Policy; Great Recession. (search for similar items in EconPapers)
JEL-codes: C11 C22 C53 E37 E52 (search for similar items in EconPapers)
Date: 2015-11-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-mac
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https://mpra.ub.uni-muenchen.de/68594/1/MPRA_paper_68594.pdf original version (application/pdf)

Related works:
Journal Article: Nowcasting in real time using popularity priors (2020) Downloads
Working Paper: Nowcasting in Real Time Using Popularity Priors (2020) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:68594

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