Forecasting travellers in Spain with Google’s search volume indices

Maximo Camacho and Matías Pacce

Tourism Economics, 2018, vol. 24, issue 4, 434-448

Abstract: We examine whether Google’s search volume indices help economic agents with real-time predictions about the checked-in and overnight stays of travellers in Spain. Using a dynamic factor approach and a real-time database of vintages that reproduces the exact information that was available to a forecaster at each particular point in time, we show that the models, including Google’s query volume indices, outperform models that exclude these leading indicators. In this way, we are the first in finding conclusive evidence that tourism-related queries help to improve tourism forecast in Spain. Our finding is of significance in this literature, since Spain is one of the world’s top tourism destinations and extremely depends on tourism.

Keywords: big data analysis; time series; tourism (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1354816617737227 (text/html)

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:sae:toueco:v:24:y:2018:i:4:p:434-448

DOI: 10.1177/1354816617737227

Access Statistics for this article

More articles in Tourism Economics
Bibliographic data for series maintained by SAGE Publications (sagediscovery@sagepub.com).