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

TL;DR

This paper introduces a multiple inflated negative binomial hurdle regression model to analyze Italian residents' tourism behavior during the 2008 Great Recession, accounting for spikes in overnight stays and a two-stage decision process.

Contribution

The paper develops a novel regression model that captures multiple distributional spikes and a two-stage decision process in tourism data, improving upon standard hurdle models.

Findings

Recession negatively affected holiday decision and duration.

The proposed model better fits the distribution of overnight stays.

Model can assist policymakers in forecasting tourism impacts.

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.