Nowcasting in Real Time Using Popularity Priors

George Monokroussos and Yongchen Zhao

No 2020-01, Working Papers from Towson University, Department of Economics

Abstract: We construct a "Google Recession Index" (GRI) using Google Trends data on internet search popularity, which tracks the public's attention to recession-related keywords in real time. We then compare nowcasts made with and without this index using both a standard dynamic factor model and a Bayesian approach with alternative prior setups. Our results indicate that using the Bayesian model with GRI-based "popularity priors" we could identify the 2008Q3 turning point in real time, without sacrificing the accuracy of the nowcasts over the rest of the sample periods.

Keywords: 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)
Pages: 19 pages
Date: 2020-02, Revised 2020-02
New Economics Papers: this item is included in nep-big, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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http://webapps.towson.edu/cbe/economics/workingpapers/2020-01.pdf First version, 2020 (application/pdf)

Related works:
Journal Article: Nowcasting in real time using popularity priors (2020)
Working Paper: Nowcasting in Real Time Using Popularity Priors (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:tow:wpaper:2020-01

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