Forecasting Tourist Arrivals in Prague: Google Econometrics

Zeynalov, Ayaz (2017): Forecasting Tourist Arrivals in Prague: Google Econometrics.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Google trends, Mixed-data sampling, forecasting, tourism
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; E - Macroeconomics and Monetary Economics > E1 - General Aggregative Models > E17 - Forecasting and Simulation: Models and Applications; L - Industrial Organization > L8 - Industry Studies: Services > L83 - Sports ; Gambling ; Restaurants ; Recreation ; Tourism

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