Forecasting tourism arrivals with an online search engine data: A study of the Balearic Islands

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dc.contributor Lucena Pimentel, Abel Ernesto García Rodríguez, Óscar 2016 2018-06-04T12:02:09Z 2018-06-04
dc.description.abstract [eng] This study explores issues related to the forecasting in revenue management in the Mechanism of predicting tourism arrivals for the Balearic Islands. Specifically, the study uses queries from a web search data (Google Trends) in order to demonstrate the forecasting power of such measures compared to traditional methods. I developed a database formed by the two main tourist volumes, namely, Germany and UK and then, compare each model with its corresponding baseline to figure out whether the Google Trends indicator can increase accuracy of the prediction. After estimating the four different models, I selected the baseline model for Germany, and the alternative for UK. Consequently, Granger causality test indicated a positive causality between variables suggesting good estimating results. Besides, I calculated the Mean Absolute Percentage Errors (MAPE) for each model and the results showed a considerable improvement of the Google Trends models compared to baseline models. The results provide some hints for increasing company efficiency and enhance policy maker decision making. ca
dc.format application/pdf
dc.language.iso eng ca
dc.publisher Universitat de les Illes Balears
dc.rights info:eu-repo/semantics/openAccess
dc.rights all rights reserved
dc.subject 338 - Situació econòmica. Política econòmica. Gestió, control i planificació de l'economia. Producció. Serveis. Turisme. Preus ca
dc.title Forecasting tourism arrivals with an online search engine data: A study of the Balearic Islands ca
dc.type info:eu-repo/semantics/masterThesis ca
dc.type info:eu-repo/semantics/publishedVersion 2018-05-31T10:06:47Z info:eu-repo/date/embargoEnd/2050-01-01
dc.embargo 2050-01-01
dc.rights.accessRights info:eu-repo/semantics/embargoedAccess

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