| 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. |