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Model Uncertainty in Characterizing Recreation Demand

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  • Babatunde Abidoye

    ()

  • Joseph Herriges

    ()

Abstract

A Bayesian variable selection procedure is used to control for uncertainty in the specification of a recreational demand model. Specifically, we propose a model that draws on the Bayesian paradigm to integrate the variable selection process into model estimation and to reflect the accompanying uncertainty about which is the best specification in subsequent counterfactual predictions. The advantage of this procedure over previous non-Bayesian approaches is that it overcomes the problem of pre-testing in specification searches. In our application, evaluating demand for recreational lake usage in Iowa, we find clear evidence that site attributes, such as lakes size, handicap facilities and wake restrictions, do impact lake usage. There is also evidence that water quality matters in household recreation choices. Indeed, contrary to Abidoye et al. (Am J Agricult Econ, 2012 ), in which only a single functional form is considered, we find clear evidence that water quality matters, with posterior probability of less that 10 % associated with a model without any water quality variables. This suggests that the flexibility that the Bayesian variable selection model affords in capturing the linkage between recreation demand and site characteristics can be important. Copyright Springer Science+Business Media B.V. 2012

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File URL: http://hdl.handle.net/10.1007/s10640-012-9561-7
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Bibliographic Info

Article provided by European Association of Environmental and Resource Economists in its journal Environmental and Resource Economics.

Volume (Year): 53 (2012)
Issue (Month): 2 (October)
Pages: 251-277

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Handle: RePEc:kap:enreec:v:53:y:2012:i:2:p:251-277

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Web page: http://www.springerlink.com/link.asp?id=100263

Related research

Keywords: Bayesian econometrics; Recreation demand; Nonmarket valuation;

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References

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  1. Carmen Fernández & Eduardo Ley & Mark F. J. Steel, 2002. "Bayesian modelling of catch in a north-west Atlantic fishery," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(3), pages 257-280.
  2. Abidoye, Babatunde & Herriges, Joseph A. & Tobias, Justin, 2010. "Controlling for Observed and Unobserved Site Characteristics in Rum Models of Recreation Demand," Staff General Research Papers 31559, Iowa State University, Department of Economics.
  3. John A. Downing, 2009. "Valuing Water Quality as a Function of Water Quality Measures," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(1), pages 106-123.
  4. Leamer, Edward E, 1983. "Let's Take the Con Out of Econometrics," American Economic Review, American Economic Association, vol. 73(1), pages 31-43, March.
  5. Koop, Gary & Tole, Lise, 2004. "Measuring the health effects of air pollution: to what extent can we really say that people are dying from bad air?," Journal of Environmental Economics and Management, Elsevier, vol. 47(1), pages 30-54, January.
  6. Herriges, Joseph A. & Phaneuf, Daniel J., 2002. "Inducing Patterns Correlation and Substitution in Repeated Logit Model of Recreation Demand," Staff General Research Papers 5035, Iowa State University, Department of Economics.
  7. Murdock, Jennifer, 2006. "Handling unobserved site characteristics in random utility models of recreation demand," Journal of Environmental Economics and Management, Elsevier, vol. 51(1), pages 1-25, January.
  8. Dale J. Poirier, 1995. "Intermediate Statistics and Econometrics: A Comparative Approach," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262161494, December.
  9. David Layton & S. Lee, 2006. "Embracing Model Uncertainty: Strategies for Response Pooling and Model Averaging," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 34(1), pages 51-85, 05.
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Cited by:
  1. Ian Bateman, 2013. "EAERE Award for the Best Paper Published in Environmental and Resource Economics During 2012," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 56(1), pages 1-2, September.

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