1991
DOI: 10.2307/1242838
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Using Count Data Models in Travel Cost Analysis with Aggregate Data

Abstract: In order to control for censoring and the integer nature of trip demand, the use of count data models in travel cost analysis is attractive. Two such models, the Poisson and negative binomial, are discussed. Robust estimation techniques that loosen potentially stringent distributional assumptions are also reviewed. For illustrative purposes, several count data models are used to estimate a county-level travel cost model using permit data from the Boundary Waters Canoe Area.

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Cited by 168 publications
(81 citation statements)
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“…The travel cost coefficient (tcost) is negative, consistently with the economic theory. This means that as travel cost increases, the number of trips decreases, as expected from the travel-cost sensitivity assumption (Hellerstein 1991). The positive and statistically significant coefficient for Tuscany indicates that people living in Tuscany region are more likely to visit Monte Morello peri-urban forest, which is reasonable since the study area is in the same region.…”
Section: Concerning Recreational Benefits Provided Bymentioning
confidence: 60%
“…The travel cost coefficient (tcost) is negative, consistently with the economic theory. This means that as travel cost increases, the number of trips decreases, as expected from the travel-cost sensitivity assumption (Hellerstein 1991). The positive and statistically significant coefficient for Tuscany indicates that people living in Tuscany region are more likely to visit Monte Morello peri-urban forest, which is reasonable since the study area is in the same region.…”
Section: Concerning Recreational Benefits Provided Bymentioning
confidence: 60%
“…Econometric models were introduced more recently and model the number of past trips through count data regressions, such as Poisson (Brida et al, 2012b) and Count Quantile (Brida et al, 2012c). These models are used in analogy with the travel cost analysis literature, a set of contributions that aims to estimate the demand for tourism by modelling the number of trips as function of the travel costs (Bestard and Font, 2010;Englin and Shonkwiler, 1995;Hellerstein, 1991;Hynes et al, 2009). …”
Section: Methodsmentioning
confidence: 99%
“…The Poisson distribution and its variants were used to represent the distributions of these trips. Daniel Hellerstein worked to develop this approach in his PhD dissertation at Yale along with Rob Mendelsohn, and the count data approach has since been extensively applied (for example, Hellerstein, 1991;Englin et al, 1997;Shonkwiler and Shaw, 1996;Haab and McConnell, 2002).…”
Section: A Brief Historymentioning
confidence: 99%