Google Correlate and Google Trends as Nowcasting Tools for Retail Sales

María Florencia Camusso () and Ramiro Emmanuel Jorge ()
Additional contact information
María Florencia Camusso: Centro de Estudios y Servicios de la Bolsa de Comercio de Santa Fe, Universidad Nacional del Litoral
Ramiro Emmanuel Jorge: Centro de Estudios y Servicios de la Bolsa de Comercio de Santa Fe, Universidad Nacional del Litoral

Ensayos Económicos, 2021, vol. 1, issue 76, 26-45

Abstract: The paper proposes a nowcasting model for Santa Fe’s supermarkets retail sales, an indicator that is released within two months of delay, internalizing information from Google Trends and Google Correlate. The procedure identifies an array of proxy variables with high predictive ability and then uses the data in order to estimate the target series considering searching patterns. Estimations computed by the model are compared to X13-ARIMA-SEATS’s forecasts. Obtained output suggests that results are not only consistent but also more opportune that official statistical releases.

Keywords: big data; cycles; Google tools; nowcast (search for similar items in EconPapers)
JEL-codes: E27 E32 (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.bcra.gov.ar/Institucional/DescargaPDF/DownloadPDF.aspx?Id=977 Spanish version (versión en Español) (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bcr:ensayo:v:1:y:2021:i:76:p:26-45

Access Statistics for this article

Ensayos Económicos is currently edited by Germán Feldman

More articles in Ensayos Económicos from Central Bank of Argentina, Economic Research Department Contact information at EDIRC.
Bibliographic data for series maintained by Federico Grillo ().