The 2007-2008 U.S. recession: What did the real-time google trends data tell the United States?

Tao Chen; Erin Pik Ki So; Liang Wu; Isabel Kit Ming Yan

doi:10.1111/coep.12074

The 2007-2008 U.S. recession: What did the real-time google trends data tell the United States?

Tao Chen, Erin Pik Ki So, Liang Wu, Isabel Kit Ming Yan

Research output: Contribution to journal › Journal article › peer-review

26 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	395-403
Number of pages	9
Journal	Contemporary Economic Policy
Volume	33
Issue number	2
DOIs	https://doi.org/10.1111/coep.12074
Publication status	Published - 1 Apr 2015

Scopus Subject Areas

General Business,Management and Accounting
Economics and Econometrics
Public Administration

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1111/coep.12074

Cite this

@article{d44ac91c16734dbaaeda5eb8685dd5e8,

title = "The 2007-2008 U.S. recession: What did the real-time google trends data tell the United States?",

abstract = "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.",

author = "Tao Chen and So, {Erin Pik Ki} and Liang Wu and Yan, {Isabel Kit Ming}",

note = "Publisher Copyright: {\textcopyright} 2014 Western Economic Association International.",

year = "2015",

month = apr,

day = "1",

doi = "10.1111/coep.12074",

language = "English",

volume = "33",

pages = "395--403",

journal = "Contemporary Economic Policy",

issn = "1074-3529",

publisher = "Wiley-Blackwell Publishing Ltd",

number = "2",

}

TY - JOUR

T1 - The 2007-2008 U.S. recession

T2 - What did the real-time google trends data tell the United States?

AU - Chen, Tao

AU - So, Erin Pik Ki

AU - Wu, Liang

AU - Yan, Isabel Kit Ming

PY - 2015/4/1

Y1 - 2015/4/1

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=84922931383&partnerID=8YFLogxK

U2 - 10.1111/coep.12074

DO - 10.1111/coep.12074

M3 - Journal article

AN - SCOPUS:84922931383

SN - 1074-3529

VL - 33

SP - 395

EP - 403

JO - Contemporary Economic Policy

JF - Contemporary Economic Policy

IS - 2

ER -

The 2007-2008 U.S. recession: What did the real-time google trends data tell the United States?

Abstract

Scopus Subject Areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this