Latent class count models of total visitation demand: Days out hiking in the eastern Alps

Environmental and Resource Economics

Volumn 38, Issue 4, 2007, Pages 447-460

  Scarpa, Riccardo ^a   Thiene, Mara ^b   Tempesta, Tiziano ^b  

^a UNIVERSITY OF WAIKATO   (New Zealand)

^b UNIVERSITY OF PADOVA   (Italy)

Author keywords

Count models; Destination choice modelling; Latent class analysis; Preference heterogeneity; Travel cost method

Indexed keywords

ECONOMIC AND SOCIAL EFFECTS; MATHEMATICAL MODELS; POISSON DISTRIBUTION;

COUNT MODELS; DESTINATION CHOICE MODELING; LATENT CLASS ANALYSIS; PREFERENCE HETEROGENEITY; TRAVEL COST METHOD;

ENVIRONMENTAL ENGINEERING;

ESTIMATION METHOD; MIXING; MODELING; POISSON RATIO; RECREATIONAL ACTIVITY; SOCIOECONOMIC CONDITIONS;

ALPS; EASTERN ALPS; EURASIA; EUROPE; ITALY; SOUTHERN EUROPE;

EID: 36249007445     PISSN: 09246460     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10640-007-9087-6     Document Type: Article

References (19)

1. Cameron A, Trivedi P (1998) Regression analysis of count data. Econometrics Society Monograph, Cambridge University Press, UK
2. Creel MD, Loomis JB (1990) Theoretical and empirical advantages of truncated count data estimators for analysis of deer hunting in California. Am J Agric Econ 72:434-441
3. Grijalva T, Berrens R, Shaw W (2002) Testing the validity of contingent behaviour trip responses. Am J Agric Econ 84:401-414
4. Gurmu S, Trivedi P (1996) Excess zero count models for recreational trips. J Bus Econ Stat 75:469-477
5. Hellerstein D, Mendelsohn M (1993) A theoretical foundation for count data models, with an application to atravel cost model. Am J Agric Econ 75:604-611
6. Hurvich M, Tsai C (1989) Regression and time series model selection in small samples. Biometrika 76:297-307
7. Kamakura WA, Wedel M (1995) Lifestyle segmentation with tailored interviewing. J Mark Res 32:308-317
8. Langeheine R, Rost J (1988) Latent trait and latent class models. Plenum, New York
9. Leroux B (1992) Consistent estimation of mixing distributions. Ann Stat 20:1350-1360
10. McLachlan G, Peel D (2000) Finite mixture models. Wiley, New York
11. Morey E, Thacher J, Breffle W (2006) Using angler characteristics and attitudinal data to identify environmental preference classes: a latent-class model. Environ Resour Econ 34:91-115
12. Phaneuf D, Kling C, Herriges J (2000) Estimation and welfare calculations in a generalized corner solution model with an application to recreation demand. Rev Econ Stat 82:83-92
13. Provencher B, Barenklau K, Bishop R (2002) A finite mixture logit model of recreational angling with serially correlated random utility. Am J Agric Econ 84:1066-1075
14. Provencher B, Bishop R (2004) Does accounting for preference heterogeneity improve the forecasting of a random utility model? A case study. J Environ Econ Manage 48:793-810
15. Scarpa R, Thiene M (2005) Destination choice models for rock climbing in the Northeastern Alps: a latent-class approach based on intensity of preference. Land Econ 85(3):426-44
16. Shonkwiler J, Shaw W (2003) A finite mixture approach to analyzing income effects in random utility models: reservoir recreation along the Columbia river. In: Hanley N, Shaw W, Wright R (eds) The new economics of outdoor recreation. Edward Elgar, UK
17. Train K (1998) Recreation demand models with taste differences over people. Land Econ 74:185-194
18. Von Haefen RH, Phaneuf DJ (2005) Kuhn-Tucker demand system approaches to non-market valuation. In: Scarpa R, Alberini A (eds). Applications of simulation method in environmental and resource economics. Springer, Dordrecht, The Netherlands, pp 135-158
19. Wedel M, Kamakura WA (2000) Market segmentation: conceptual methodological foundations. Kluwer, Boston