A multiple inflated negative binomial hurdle regression model: analysis of the Italians’ tourism behaviour during the Great Recession

Chiara Bocci (), Laura Grassini () and Emilia Rocco ()
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Chiara Bocci: University of Florence
Laura Grassini: University of Florence
Emilia Rocco: University of Florence

Statistical Methods & Applications, 2021, vol. 30, issue 4, No 1, 1109-1133

Abstract: Abstract We analyse tourism behaviour of Italian residents in the period covering the 2008 Great Recession. Using the Trips of Italian Residents in Italy and Abroad quarterly survey, carried out by the Italian National Institute of Statistics, we investigate whether and how the economic recession has affected the total number of overnight stays. The response variable is the result of a two-stage decision process: first we choose to take a holiday, then for how long. Moreover, since the number of overnight stays is typically concentrated on specific lengths (week-end, week, fortnight) we observe multiple peculiar spikes in its distribution. To take into account these two distinctive characteristics, we generalise the usual hurdle regression model by specifying a multiple inflated truncated negative binomial distribution for the positive responses. Results show that the economic recession impacted negatively on both components of the decision process and that, by controlling for the inflated nature of the response variable’s distribution, the proposed formulation provides a better representation of the Italians’ tourism behaviour in comparison with non-inflated hurdle models. Given this, we believe that our model can be a useful tool for policy makers who are trying to forecast the effects of new targeted policies to support tourism economy.

Keywords: Count data; Multinomial logit; Heterogeneous negative binomial; Multimodal distribution; Overdispersed data; Truncated-at-zero models (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10260-020-00542-6

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