Can internet searches forecast tourism inflows?

Abstract

Purpose – The purpose of this paper is to improve the forecast of tourism inflows into Spain by use of Google – indices on internet searches measuring the relative popularity of keywords associated with travelling to Spain. Design/methodology/approach – Two models are estimated for each of the three countries with the largest tourist flows into Spain (Germany, UK and France): a conventional model, the best ARIMA model estimated by TRAMO (model 0) and a model augmented with the Google-index relating to searches made from each country (model 1). The overall performance of both models is compared. Findings – The improvement in forecasting provided by the short-term models that include the G-indicator is quite substantial up to 2012, reducing out of sample mean square errors by 42 per cent, although their performance worsens in the following years. Research limitations/implications – Deeper study and conceptualization of sources of error in Google trends and data quality is necessary. Originality/value – The paper illustrates that while this new tool can be a powerful instrument for policy makers as a valuable and timely complement for traditional statistics, further research and better access to data is needed to better understand how internet consumers’ search activities translate (or not) into actual economic outcomes.