Forecasting GDP growth rates in the United States and Brazil using Google Trends

Evripidis Bantis, Michael Clements and Andrew Urquhart

International Journal of Forecasting, 2023, vol. 39, issue 4, 1909-1924

Abstract: In this paper we consider the value of Google Trends search data for nowcasting (and forecasting) GDP growth for a developed economy (the U.S.) and an emerging-market economy (Brazil). Our focus is on the marginal contribution of big data in the form of Google Trends data over and above that of traditional predictors, and we use a dynamic factor model to handle the large number of potential predictors and the “ragged-edge” problem. We find that factor models based on economic indicators and Google “categories” data provide gains compared to models that exclude this information. The benefits of using Google Trends data appear to be broadly similar for Brazil and the U.S., and depend on the factor model variable-selection strategy. Using more disaggregated Google Trends data than its “categories” is not beneficial.

Keywords: Big data; Google Search; Factor models; Nowcasting; Variable selection (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207022001418
Full text for ScienceDirect subscribers only

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:eee:intfor:v:39:y:2023:i:4:p:1909-1924

DOI: 10.1016/j.ijforecast.2022.10.003

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().