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Forecasting Tourist Arrivals in Prague: Google Econometrics

Ayaz Zeynalov

MPRA Paper from University Library of Munich, Germany

Abstract: It is expected that what people are searching for today is predictive of what they have done recently or will do in the near future. This study analyzes the reliability of Google search data in predicting tourist arrivals and overnight stays in Prague. Three differ- ently weighted weekly Mixed-data sampling (MIDAS) models, ARIMA(1,1,1) with Monthly Google Trends information and model without informative Google Trends variable have been evaluated. The main objective was to assess whether Google Trends information is useful for forecasting tourist arrivals and overnight stays in Prague, as well as whether higher fre- quency data (weekly data) outperform same frequency data methods. The results of the study indicate an undeniable potential that Google Trends offers more accurate forecast- ing, particularly for tourism. The forecasting of the indicators using weekly MIDAS-Beta for tourist arrivals and weekly MIDAS-Almon models for overnight stays outperformed monthly Google Trends using ARIMA and models without Google Trends. The results confirm that predications based on Google searches are advantageous for policy makers and business operating in the tourism sector.

Keywords: Google trends; Mixed-data sampling; forecasting; tourism (search for similar items in EconPapers)
JEL-codes: C53 E17 L83 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-for, nep-ict and nep-tur
Date: 2017-12-01
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