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THE 2007–2008 U.S. RECESSION: WHAT DID THE REAL‐TIME GOOGLE TRENDS DATA TELL THE UNITED STATES?
Author(s) -
Chen Tao,
So Erin Pik Ki,
Wu Liang,
Yan Isabel Kit Ming
Publication year - 2015
Publication title -
contemporary economic policy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.454
H-Index - 49
eISSN - 1465-7287
pISSN - 1074-3529
DOI - 10.1111/coep.12074
Subject(s) - business cycle , recession , aggregate (composite) , economics , identification (biology) , turning point , extant taxon , economic indicator , real time data , econometrics , point (geometry) , macroeconomics , computer science , world wide web , period (music) , botany , materials science , physics , geometry , mathematics , evolutionary biology , biology , acoustics , composite material
In the extant literature of business cycle predictions, the signals for business cycle turning points are generally issued with a lag of at least 5 months. In this paper, we make use of a novel and timely indicator—the Google search volume data—to help to improve the timeliness of business cycle turning point identification. We identify multiple query terms to capture the real‐time public concern on the aggregate economy, the credit market, and the labor market condition. We incorporate the query indices in a Markov‐switching framework and successfully “nowcast” the peak date within a month that the turning occurred . ( JEL E37, G17)