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- W578313277 abstract "Focusing on tourist behavior, this paper develops a nested time use and expenditure behavior model in the context of multi-destination visit, where a tourist visits one or more destinations. In this case, tourists' decisions include, 1) whether to visit a destination or not (destination visit decision), and in case of visiting a destination, 2) how long to stay there (activity time decision), 3) whether to spend any money there (expenditure decision), and in case of spending any money, 4) how much of money to spend there (expenditure level decision). To accommodate the above decision-making mechanism with two discrete and two continuous dependent variables, a nested Tobit modeling technique is first integrated with a multi-linear utility-maximizing time use and expenditure behavior model, and then a pair copula is applied to represent the correlated error structure of the above four dependent variables. Pair copula is a function that can combine different bivariate copulas to represent a joint multivariate distribution, where variables are sequentially incorporated into conditioning sets with a nested tree structure. As a case study, the developed model is estimated by comparing three types of canonical vine copulas: Gaussian, Farlie-Gumbel-Morgenstern (FGM) and Frank copulas. First, the model effectiveness is confirmed by using a questionnaire data collected in the Tottori Prefecture of Japan in 2007. Second, it is revealed that the Frank canonical vine pair copula model is superior to other models. Third, it is found that the value of activity time varies considerably with touristsf origins. Finally, influential factors to time use and expenditure behavior are examined." @default.
- W578313277 created "2016-06-24" @default.
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- W578313277 date "2013-01-01" @default.
- W578313277 modified "2023-09-27" @default.
- W578313277 title "Nested Tourist Time Use and Expenditure Behavior Model with Multidestination Visit Based on Pair Copula" @default.
- W578313277 hasPublicationYear "2013" @default.
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