Advanced Search
MyIDEAS: Login

The Fast Decay Process In Recreational Demand Activities And The Use Of Alternative Count Data Models

Contents:

Author Info

  • Sarker, Rakhal
  • Surry, Yves R.
Registered author(s):

    Abstract

    Since the early 1990s, researchers have routinely used count data models (such as the Poisson and negative binomial) to estimate the demand for recreational activities. Along with the success and popularity of count data models in recreational demand analysis during the last decade, a number of shortcomings of standard count data models became obvious to researchers. This had led to the development of new and more sophisticated model specifications. Furthermore, semi-parametric and non-parametric approaches have also made their way into count data models. Despite these advances, however, one interesting issue has received little research attention in this area. This is related to the fast decay process of the dependent variable and the associated long tail. This phenomenon is observed quite frequently in recreational demand studies; most recreationists make one or two trips while a few of them make exceedingly large number of trips. This introduces an extreme form of overdispersion difficult to address in popular count data models. The major objective of this paper is to investigate the issues related to proper modelling of the fast decay process and the associated long tails in recreation demand analysis. For this purpose, we introduce two categories of alternative count data models. The first group includes four alternative count data models, each characterised by a single parameter while the second group includes one count data model characterised by two parameters. This paper demonstrates how these alternative models can be used to properly model the fast decay process and the associated long tail commonly observed in recreation demand analysis. The first four alternative count data models are based on an adaptation of the geometric, Borel, logarithmic and Yule probability distributions to count data models while the second group of models relied on the use of the generalised Poisson probability distribution. All these alternative count data models are empirically implemented using the maximum likelihood estimation procedure and applied to study the demand for moose hunting in Northern Ontario. Econometric results indicate that most of the alternative count data models proposed in this paper are able to capture the fast decay process characterising the number of moose hunting trips. Overall they seem to perform as well as the conventional negative binomial model and better than the Poisson specification. However further investigation of the econometric results reveal that the geometric and generalised Poisson model specifications fare better than the modified Borel and Yule regression models.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://purl.umn.edu/34147
    Download Restriction: no

    Bibliographic Info

    Paper provided by University of Guelph, Department of Food, Agricultural and Resource Economics in its series Working Papers with number 34147.

    as in new window
    Length:
    Date of creation: 2003
    Date of revision:
    Handle: RePEc:ags:uguewp:34147

    Contact details of provider:
    Web page: http://fare.uoguelph.ca/
    More information through EDIRC

    Related research

    Keywords: Fast Decay Process; Recreational Demand; Count Data Models; Borel; Yule; logarithmic and generalised Poisson regression models; Resource /Energy Economics and Policy;

    References

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
    as in new window
    1. 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 104-12, February.
    2. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    3. Joseph C. Cooper, 2000. "Nonparametric and Semi-Nonparametric Recreational Demand Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(2), pages 451-462.
    4. Hausman, Jerry A. & Leonard, Gregory K. & McFadden, Daniel, 1995. "A utility-consistent, combined discrete choice and count data model Assessing recreational use losses due to natural resource damage," Journal of Public Economics, Elsevier, vol. 56(1), pages 1-30, January.
    5. Hellerstein, Daniel & Mendelsohn, Robert, 1993. "A Theoretical Foundation for Count Data Models," MPRA Paper 25265, University Library of Munich, Germany.
    6. Winfried Pohlmeier & Volker Ulrich, 1995. "An Econometric Model of the Two-Part Decisionmaking Process in the Demand for Health Care," Journal of Human Resources, University of Wisconsin Press, vol. 30(2), pages 339-361.
    7. 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.
    8. Delgado, Miguel A & Robinson, Peter M, 1992. " Nonparametric and Semiparametric Methods for Economic Research," Journal of Economic Surveys, Wiley Blackwell, vol. 6(3), pages 201-49.
    9. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
    10. Michael D. Creel, 1997. "Welfare Estimation Using The Fourier Form: Simulation Evidence For The Recreation Demand Case," The Review of Economics and Statistics, MIT Press, vol. 79(1), pages 88-94, February.
    11. Grogger, J T & Carson, Richard T, 1991. "Models for Truncated Counts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(3), pages 225-38, July-Sept.
    12. Hellerstein, Daniel, 1991. "Using Count Data Models in Travel Cost Analysis with Aggregate Data," MPRA Paper 25264, University Library of Munich, Germany.
    13. Shaw, W. Douglass & Jakus, Paul M., 1996. "Travel Cost Models Of The Demand For Rock Climbing," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 25(2), October.
    14. Joseph Cooper & John Loomis, 1993. "Testing whether waterfowl hunting benefits increase with greater water deliveries to wetlands," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 3(6), pages 545-561, December.
    15. Cameron, A Colin & Johansson, Per, 1997. "Count Data Regression Using Series Expansions: With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 203-23, May-June.
    16. 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 225-43, May-June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:ags:uguewp:34147. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search).

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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