Comparing forecasts for tourism dynamics in Medellín, Colombia
Marisol Valencia Cárdenas (),
Juan Gabriel Vanegas López (),
Juan Carlos Correa Morales () and
Jorge Aníbal Restrepo Morales ()
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Marisol Valencia Cárdenas: Institución Universitaria Tecnológico de Antioquia
Juan Gabriel Vanegas López: Fundación Universitaria Autónoma de las Américas
Juan Carlos Correa Morales: Universidad Nacional de Colombia
Jorge Aníbal Restrepo Morales: Institución Universitaria Tecnológico de Antioquia
Lecturas de Economía, 2017, issue 86, 199-230
Tourism is a topic of interest to many economies around the world, but it has received limited attention in Colombia. Knowing the periods of larger tourist inflows is important for predicting coverage in services for tourists. In this paper, we compare the estimation between classical and Bayesian regression in order to choose the best alternative to predict the number of tourist arrivals to Medellin. We also identify the most significant variables affecting the influx of tourists and the models providing better fit to the associated dynamics. According to our results, the Bayesian approach shows better estimates than the classic one. In addition, the variable month is significant to explain the demands for both Colombians and foreigners. The periods with the highest incidence of visits to the city are December-January and June-July, a pattern that repeats itself every year, which is crucial for planning hotel resources
Keywords: tourism demand; model evaluation and selection; forecasting and prediction methods; Bayesian statistics; Medellín (search for similar items in EconPapers)
JEL-codes: L83 C13 C52 C53 C65 R11 (search for similar items in EconPapers)
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