Date of Award
5-2014
Degree Type
Report
Degree Name
Master of Science (MS)
Department
Economics and Finance
Committee Chair(s)
Devon Gorry
Committee
Devon Gorry
Committee
William F. Shughart II
Committee
Diana Thomas
Abstract
This paper seeks to demonstrate that the incorporation of search statistics in autoregressive models used to predict the arrivals of tourists to Punta Cana, Dominican Republic, improve the predictive power of the models. This paper explores whether the Internet search information in “Punta Cana” in the United States and Canada between January 2004 and August 2013, reflects the behavior of this variable in real time by using nowcasting methodology that combines variables with different frequencies of time. We find that including the searches of “Punta Cana” that Canadians make on Google in the first week of a month helps predict the actual arrival of Canadian tourists to Punta Cana in that month. The same applies in the case of Americans, but using the searches made during the third week of a month.
Recommended Citation
De La Oz Pineda, Michelle E., "Predicting Tourist Inflows to Punta Cana, Dominican Republic, Using Google Trends" (2014). All Graduate Plan B and other Reports, Spring 1920 to Spring 2023. 360.
https://digitalcommons.usu.edu/gradreports/360
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