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Model Uncertainty in Characterizing Recreation Demand

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  • Abidoye, Babatunde
  • Herriges, Joseph A.

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 the model and reflect the accompanying uncertainty about which is the “best†specification used for counterfactual predictions. The advantage of this procedure over previous non-Bayesian approaches is that overcomes the problem of pre-testing in specification searches.

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File URL: http://www.econ.iastate.edu/sites/default/files/publications/papers/p12028-2010-10-12.pdf
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Bibliographic Info

Paper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number 32028.

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Date of creation: 12 Oct 2010
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Publication status: Forthcoming in Environmental and Resource Economics
Handle: RePEc:isu:genres:32028

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Postal: Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070
Phone: +1 515.294.6741
Fax: +1 515.294.0221
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Web page: http://www.econ.iastate.edu
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  1. Babatunde O. Abidoye & Joseph A. Herriges & Justin L. Tobias, 2012. "Controlling for Observed and Unobserved Site Characteristics in RUM Models of Recreation Demand," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, Agricultural and Applied Economics Association, vol. 94(5), pages 1070-1093.
  2. Carmen Fernandez & E. Ley & M. F. J. Steel, 2004. "Bayesian modelling of catch in a Northwest Atlantic Fishery," ESE Discussion Papers, Edinburgh School of Economics, University of Edinburgh 67, Edinburgh School of Economics, University of Edinburgh.
  3. Egan, Kevin J. & Herriges, Joseph A. & Kling, Catherine L. & Downing, John A., 2004. "Valuing Water Quality As a Functionof Water Quality Measures," Staff General Research Papers, Iowa State University, Department of Economics 12210, Iowa State University, Department of Economics.
  4. Edward E. Leamer, 1982. "Let's Take the Con Out of Econometrics," UCLA Economics Working Papers, UCLA Department of Economics 239, UCLA Department of Economics.
  5. 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, European Association of Environmental and Resource Economists, vol. 34(1), pages 51-85, 05.
  6. 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.
  7. Herriges, Joseph A. & Phaneuf, Daniel J., 2002. "Inducing Patterns Correlation and Substitution in Repeated Logit Model of Recreation Demand," Staff General Research Papers, Iowa State University, Department of Economics 5035, Iowa State University, Department of Economics.
  8. Dale J. Poirier, 1995. "Intermediate Statistics and Econometrics: A Comparative Approach," MIT Press Books, The MIT Press, The MIT Press, edition 1, volume 1, number 0262161494, December.
  9. 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.
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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, European Association of Environmental and Resource Economists, vol. 56(1), pages 1-2, September.

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