THE 2007-2008 U.S. RECESSION: WHAT DID THE REAL-TIME GOOGLE TRENDS DATA TELL THE UNITED STATES?
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 tur...
| Publicado en: | Contemporary Economic Policy Vol. 33; no. 2; pp. 395 - 404 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Wiley-Blackwell
Apr2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=100824280&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 100824280 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10743529 CEY jtl: Contemporary Economic Policy issn: 10743529 maglogo: Y pubinfo: dt: Apr2015 vid: 33 iid: 2 pid: 480 pub: Wiley-Blackwell artinfo: ui: 100824280 10.1111/coep.12074 ppf: 395 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P size: 659KB tig: atl: THE 2007-2008 U.S. RECESSION: WHAT DID THE REAL-TIME GOOGLE TRENDS DATA TELL THE UNITED STATES? aug: au: Chen, Tao So, Erin Pik Ki Wu, Liang Yan, Isabel Kit Ming affil: Lee Shau Kee School of Business & Administration, Open University of Hong Kong, 30 Good Shepherd Street, Ho Man Tin, Kowloon, Hong Kong China Department of Economics, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong China Department of Economics and Finance, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China su: United States Google Inc. Recessions Business cycles Labor market Real-time computing Twentieth century sug: subj: Recessions Business cycles Labor market United States Google Inc. Real-time computing Twentieth century ab: 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) pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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