## Abstract This paper contributes in three dimensions to the literature on health care demand. First, it features the first application of a bivariate random effects estimator in a count data setting, to permit the efficient estimation of this type of model with panel data. Second, it provides an
A bivariate count data model for household tourism demand
✍ Scribed by Jörgen Hellström
- Publisher
- John Wiley and Sons
- Year
- 2006
- Tongue
- English
- Weight
- 126 KB
- Volume
- 21
- Category
- Article
- ISSN
- 0883-7252
- DOI
- 10.1002/jae.812
No coin nor oath required. For personal study only.
✦ Synopsis
Abstract
Households' choice of the number of leisure trips and the total number of overnight stays is empirically studied using Swedish tourism data. A bivariate hurdle approach separating the participation (to travel and stay the night or not) from the quantity (the number of trips and nights) decision is employed. The quantity decision is modelled with a bivariate mixed Poisson lognormal model allowing for both positive as well as negative correlation between count variables. The observed endogenous variables are drawn from a truncated density and estimation is pursued by simulated maximum likelihood. The estimation results indicate a negative correlation between the number of trips and nights. In most cases own price effects are as expected negative, while estimates of cross‐price effects vary between samples. Copyright © 2005 John Wiley & Sons, Ltd.
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