Skewed Binary Regression to Study Rental Cars by Tourists in the Canary Islands

Abstract

Tourism is one of the economic sectors that contributes the most to the gross domestic product in many countries, moving, in turn, other economic sectors such as transport. In particular, the automotive industry constitutes an economic subsector that moves vast amounts of money. Concerning tourism and transport sectors, car rental is a crucial element contributing considerably to gross domestic product and job creation. Due to the effects that vehicle rental seems to have on various economic sectors, it seems interesting to know why a tourist chooses to rent a car during their vacation in a specific destination. This work aims to study those factors that can be considered relevant and affect the probability of renting a vehicle. The document addressed the following research topics: (a) identifying significant variables; and (b) can information on these factors help car rental firms? Empirically, it is shown that more tourists do not rent a car and this fact has to be considered. Thus, the classical logistic and Bayesian regression models do not seem adequate in this case, so that the authors will consider an asymmetric logistic regression model. This work analyzes 28,235 tourists who visited the Canary Islands during 2017. From a Bayesian point of view, asymmetric logistics regression is chosen as the best model because it detects relevant development factors not seen by standard logistic regressions. In light of the document’s findings, various practice recommendations improve decision-making in this field. The asymmetric logit link is a helpful device that can help rental companies make decisions about their clients.