Modeling Trip Count Data with Excess Zeros for U.S. Saltwater Recreational Fishing

Journal of Economics and Business

ISSN 2615-3726 (Online)

ISSN 2621-5667 (Print)

Published: 15 August 2019

Modeling Trip Count Data with Excess Zeros for U.S. Saltwater Recreational Fishing

Yeong Nain Chi, Guang-Hwa Andy Chang

University of Maryland Eastern Shore, Youngstown State University

Download Full-Text Pdf

10.31014/aior.1992.02.03.126

Pages: 773-785

Keywords: Count Data, Excess Zeros, Hurdle Poisson Model, Negative Binomial Model, Over-Dispersion, Poisson Model, Saltwater Recreational Fishing Trips, Zero-Inflated Models, Zero-Truncated Models

Abstract

Count data, such as recreational fishing trips taken by anglers, is increasingly common in recreational fishing demand analysis. Because of the non-negative integer nature of the recreational fishing trip data, some over-dispersion problems, and truncation of the data at zero trips, count data models are more appropriate for estimating the recreational fishing demand function. This study employed count data models to analyze U.S. saltwater recreational fishing trips with excess zeros, using a cross-sectional data extracted from the 2011 National Survey of Fishing, Hunting, and Wildlife Associated Recreation. Using Akaike Information Criterion and Bayesian Information Criterion, the zero-truncated negative binomial model was selected among other count data models better fitted in this count data for this study. Empirical results of this study provide insight into the determinants of saltwater recreational fishing trips, which can be used in analyzing the social and economic values of saltwater recreational fisheries management.

References

  1. Cisneros-Montemayor, A. M. and Sumaila, U. R. (2010). A global estimate of benefits from ecosystem-based marine recreation: potential impacts and implications for management. Journal of Bioeconomics 12(3), 245-268.

  2. Cohen, A. C., Jr. (1960). Estimating the parameter in a conditional Poisson distribution. Biometrics 16(2), 203-211. 

  3. David, F. N. and Johnson, N. L. (1952). Extension of a method of investigating the properties of analysis of variance tests to the case of random and mixed Models. The Annals of mathematical Statistics 23(4), 594-601.

  4. Good, I. J. (1986). Some statistical applications of Poisson’s work. Statistical Science 1(2), 157-180.

  5. Greene, W. H. (2008). Econometric analysis (Sixth Ed.). Upper Saddle River, New Jersey: Pearson Education Inc.

  6. Greene, W. H. (1994). Accounting for excess zeros and sample selection in Poisson and negative binomial regression models. NYU Working Paper No. EC-94-10. Available online at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1293115

  7. Grogger, J. and Carson, R. (1991). Models for truncated counts. Journal of Applied Econometrics 6(3), 225-238.

  8. Gurmu, S. (1991). Tests for detecting overdispersion in the positive Poisson regression model. Journal of Business & Economic Statistics 9(2), 215-222.

  9. Hall, D. B. (2000). Zero-inflated Poisson and binomial regression with random effects: a case study. Biometrics 56(4), 1030-1039.

  10. Hardin, J. W. and Hilbe, J. M. (2012). Generalized linear models and extensions (Third Ed.). College Station, Texas: A Stata Press Publication.

  11. Heilbron, D. C. (1994). Zero-altered and other regression models for count data with added zeros. Biometrical Journal 36(5), 531–547.

  12. Singh, J. (1978). A characterization of positive Poisson distribution and its application. SIAM Journal on Applied Mathematics 34(3), 545-548.

  13. Lambert, D. (1992). Zero-inflated Poisson regression with an application to defects in manufacturing. Technometrics 34(1), 1-14.

  14. Loeys, T., Moerkerke, B., Smet, O. D. and Buysse, A. (2012). The analysis of zero-inflated count data: beyond zero-inflated Poisson regression. British Journal of Mathematical and Statistical Psychology 65(1), 163-180.

  15. Mullahy, J. (1986). Specification and testing of some modified count data models. Journal of Econometrics 33(3), 341-365.

  16. Potts, J. M. and Elith, J. (2006). Comparing species abundance models. Ecological Modelling 199(2), 153-163.

  17. Sampford, M. R. (1955). The truncated negative binomial distribution. Biometrika 42(1-2), 58-69.

  18. Shonkwiler, J. S. and Shaw, W. D. (1996). Hurdle count-data models in recreation demand analysis. Journal of Agricultural and Resource Economics 21(2), 210-219.

  19. U.S. Department of the Interior, U.S. Fish and Wildlife Service, and U.S. Department of Commerce, U.S. Census Bureau. (2002). 2001 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation. Available online at https://www.census.gov/prod/2002pubs/FHW01.pdf [accessed 2 October 2018]

  20. U.S. Department of the Interior, U.S. Fish and Wildlife Service, and U.S. Department of Commerce, U.S. Census Bureau. (2007). 2006 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation. Available online at https://www.census.gov/prod/2008pubs/fhw06-nat.pdf [accessed 2 October 2018]

  21. U.S. Department of the Interior, U.S. Fish and Wildlife Service, and U.S. Department of Commerce, U.S. Census Bureau. (2014). 2011 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation. Available online at https://www.census.gov/prod/2012pubs/fhw11-nat.pdf [accessed 2 October 2018]

  22. Zawacki, W. T., Marsinko, A. and Bowker, J. M. (2000). A travel cost analysis of nonconsumptive wildlife-associated recreation in the United States. Forest Science 46(4), 496-506.

About Us

The Asian Institute of Research is an online and open-access platform to publish recent research and articles of scholars worldwide. Founded in 2018 and based in Indonesia, the Institute serves as a platform for academics, educators, scholars, and students from Asia and around the world, to connect with one another. The Institute disseminates research that is proven or predicted to be of significant influence for the general public.

Contact Us

Please send all inquiries to the email:

editorial@asianinstituteofresearch.org

Business Address:

5th Floor, Kavling 507, Fajar Graha Pena Tower, Jl. Urip Sumohardjo No.20, Makassar, Indonesia 90234

Copyright © 2018 The Asian Institute of Research. All rights reserved

Stay Connected

  • Instagram - Black Circle
  • Facebook - Black Circle
  • LinkedIn - Black Circle