IDEAS home Printed from https://ideas.repec.org/p/ags/saeatm/35079.html
   My bibliography  Save this paper

Application Of Hurdle Negative Binomial Count Data Model To Demand For Black Bass Fishing In The Southeastern United States

Author

Listed:
  • Bilgic, Abdulbaki
  • Florkowski, Wojciech J.

Abstract

This paper identifies factors that influence the demand for a black bass fishing trip taken in the Southeastern U.S. using a double hurdle negative binomial count data model. The probability of fishing for a black bass is estimated in the first stage and the trip frequency for fishing a black bass is estimated in the second stage given that the individual has a positive probability towards undertaking a black bass fishing trip in the Southeast. The applied approach allows the decomposition of the effects of factors responsible for the decision of taking a fishing trip and the number of trips.

Suggested Citation

  • Bilgic, Abdulbaki & Florkowski, Wojciech J., 2003. "Application Of Hurdle Negative Binomial Count Data Model To Demand For Black Bass Fishing In The Southeastern United States," 2003 Annual Meeting, February 1-5, 2003, Mobile, Alabama 35079, Southern Agricultural Economics Association.
    • Handle: RePEc:ags:saeatm:35079
    DOI: 10.22004/ag.econ.35079
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/35079/files/sp03bi04.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.35079?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Michael D. Creel & John B. Loomis, 1990. "Theoretical and Empirical Advantages of Truncated Count Data Estimators for Analysis of Deer Hunting in California," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 72(2), pages 434-441.
    2. Shonkwiler, John Scott & Shaw, W. Douglass, 1996. "Hurdle Count-Data Models In Recreation Demand Analysis," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 21(2), pages 1-10, December.
    3. Jones, Andrew M, 1989. "A Systems Approach to the Demand for Alcohol and Tobacco," Bulletin of Economic Research, Wiley Blackwell, vol. 41(2), pages 85-105, April.
    4. Englin, Jeffrey & Shonkwiler, J S, 1995. "Estimating Social Welfare Using Count Data Models: An Application to Long-Run Recreation Demand under Conditions of Endogenous Stratification and Truncation," The Review of Economics and Statistics, MIT Press, vol. 77(1), pages 104-112, February.
    5. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    6. Timothy C. Haab & Kenneth E. McConnell, 1996. "Count Data Models and the Problem of Zeros in Recreation Demand Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(1), pages 89-102.
    7. Gurmu, Shiferaw, 1998. "Generalized hurdle count data regression models," Economics Letters, Elsevier, vol. 58(3), pages 263-268, March.
    8. Gurmu, Shiferaw & Trivedi, Pravin K, 1996. "Excess Zeros in Count Models for Recreational Trips," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 469-477, October.
    9. Daniel M. Hellerstein, 1991. "Using Count Data Models in Travel Cost Analysis with Aggregate Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 73(3), pages 860-866.
    10. Gurmu, Shiferaw, 1997. "Semi-Parametric Estimation of Hurdle Regression Models with an Application to Medicaid Utilization," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 225-243, May-June.
    11. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    12. Kalyan Chakraborty & John Keith, 2000. "Estimating the Recreation Demand and Economic Value of Mountain Biking in Moab, Utah: An Application of Count Data Models," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 43(4), pages 461-469.
    13. Gurmu, Shiferaw & Trivedi, Pravin K., 1992. "Overdispersion tests for truncated Poisson regression models," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 347-370.
    14. Grogger, J T & Carson, Richard T, 1991. "Models for Truncated Counts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(3), pages 225-238, July-Sept.
    15. Gurmu, Shiferaw, 1991. "Tests for Detecting Overdispersion in the Positive Poisson Regression Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(2), pages 215-222, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Isabel Mendes & Isabel Proença, 2009. "Measuring the Social Recreation Per-Day Net Benefit of Wildlife Amenities of a National Park: A Count-Data Travel Cost Approach," Working Papers Department of Economics 2009/35, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    2. Meisner, Craig & Wang, Hua & Laplante, Benoit, 2006. "Welfare measurement bias in household and on-site surveying of water-based recreation : an application to Lake Sevan, Armenia," Policy Research Working Paper Series 3932, The World Bank.
    3. Sarker, Rakhal & Surry, Yves R., 2003. "The Fast Decay Process In Recreational Demand Activities And The Use Of Alternative Count Data Models," Working Papers 34147, University of Guelph, Department of Food, Agricultural and Resource Economics.
    4. Gurmu, Shiferaw & Rilstone, Paul & Stern, Steven, 1998. "Semiparametric estimation of count regression models1," Journal of Econometrics, Elsevier, vol. 88(1), pages 123-150, November.
    5. Bilgic, Abdulbaki & Florkowski, Wojciech J., 2003. "Explaning Anglers Behavior Using Count Data Models With Endogenous Switching Regime," 2003 Annual meeting, July 27-30, Montreal, Canada 22087, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    6. R. Martínez-Espiñeira, 2007. "‘Adopt a Hypothetical Pup’: A Count Data Approach to the Valuation of Wildlife," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 37(2), pages 335-360, June.
    7. Serge Garcia & Julien Jacob, 2010. "La valeur récréative de la forêt en France : une approche par les coûts de déplacement," Review of Agricultural and Environmental Studies - Revue d'Etudes en Agriculture et Environnement, INRA Department of Economics, vol. 91(1), pages 43-71.
    8. Roberto Martinez-Espineira & Joe Amoako-Tuffour, 2005. "Recreation Demand Analysis under Truncation, Overdispersion, and Endogenous Stratification: An Application to Gros Morne National Park," Econometrics 0511007, University Library of Munich, Germany.
    9. V. J. Cano Fernandez & G. Guirao Perez & M. C. Rodriguez Donate & M. E. Romero Rodriguez, 2009. "An analysis of count data models for the study of exclusivity in wine consumption," Applied Economics, Taylor & Francis Journals, vol. 41(12), pages 1563-1574.
    10. Isabel Proenca & Isabel Menes, 2000. "Measuring the Average Per Day Net Benefit of Non-consumptive Wildlife - Associated Recreation For a National Park: a Count-Data Travel Cost Approach," Regional and Urban Modeling 283600078, EcoMod.
    11. Bilgic, Abdulbaki & Florkowski, Wojciech J., 2003. "Truncated-At-Zero Count Data Models With Partial Observability: An Application To The Freshwater Fishing Demand In The Southeastern U.S," 2003 Annual Meeting, February 1-5, 2003, Mobile, Alabama 35185, Southern Agricultural Economics Association.
    12. Isabel Mendes & Isabel Proença, 2005. "Estimating the Recreation Value of Ecosystems by Using a Travel Cost Method Approach," Working Papers Department of Economics 2005/08, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    13. Abdulbaki Bilgic & Wojciech Florkowski, 2009. "The impact of license regulation on the number of recreation trips: is it worth considering?," Journal of Regulatory Economics, Springer, vol. 35(1), pages 45-69, February.
    14. Georgios Papadopoulos, 2013. "Immigration Status and Victimization: Evidence from the British Crime Survey," University of East Anglia Applied and Financial Economics Working Paper Series 042, School of Economics, University of East Anglia, Norwich, UK..
    15. Roberto Martinez-Espineira & Joe Amoako-Tuffour, 2008. "Multi-destination and multi-purpose trip effects in the analysis of the demand for trips to a remote recreational site," EERI Research Paper Series EERI_RP_2008_19, Economics and Econometrics Research Institute (EERI), Brussels.
    16. John A. Curtis, 2002. "Estimating the Demand for Salmon Angling in Ireland," The Economic and Social Review, Economic and Social Studies, vol. 33(3), pages 319-332.
    17. Shrestha, Ram K. & Seidl, Andrew F. & Moraes, Andre S., 2002. "Value of recreational fishing in the Brazilian Pantanal: a travel cost analysis using count data models," Ecological Economics, Elsevier, vol. 42(1-2), pages 289-299, August.
    18. Stefano Mainardi, 2003. "Testing convergence in life expectancies: count regression models on panel data," Prague Economic Papers, Prague University of Economics and Business, vol. 2003(4), pages 350-370.
    19. Stephen Hynes & Brian Cahill & Emma J. Dillon, 2007. "A Negative Binomial Discrete Choice Model of Forestry Recreation in Ireland," Working Papers 0709, Rural Economy and Development Programme,Teagasc.
    20. Pascoe, Sean, 2019. "Recreational beach use values with multiple activities," Ecological Economics, Elsevier, vol. 160(C), pages 137-144.

    More about this item

    Keywords

    Consumer/Household Economics;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:saeatm:35079. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/saeaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.