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A semi-parametric estimator for revealed and stated preference data--An application to recreational beach visitation

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  • Landry, Craig E.
  • Liu, Haiyong

Abstract

We present a semi-parametric approach for jointly estimating revealed and stated preference recreation demand models. The discrete factor method (DFM) allows for correlation across demand equations and incorporates unobserved heterogeneity. Our model is a generalized negative binomial with random effects; the random effect is composed of a discrete representation of unobserved heterogeneity and a factor loading that translates the heterogeneity measure into a demand effect. Our empirical application is to beach recreation demand in North Carolina. Statistical evidence supports our DFM specification, which imposes less restriction on model dispersion and incorporates unobserved heterogeneity in a flexible manner. Elasticity estimates are smaller than those derived from models with parametric specifications for unobserved heterogeneity, and welfare estimates are slightly larger (and less precise). While parametric models clearly dominate if the specification of unobserved heterogeneity is correct, the semi-parametric DFM provides a flexible alternative in cases where mis-specification is a potential problem.

Suggested Citation

  • Landry, Craig E. & Liu, Haiyong, 2009. "A semi-parametric estimator for revealed and stated preference data--An application to recreational beach visitation," Journal of Environmental Economics and Management, Elsevier, vol. 57(2), pages 205-218, March.
  • Handle: RePEc:eee:jeeman:v:57:y:2009:i:2:p:205-218
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    Cited by:

    1. Halkos, George, 2012. "The use of contingent valuation in assessing marine and coastal ecosystems’ water quality: A review," MPRA Paper 42183, University Library of Munich, Germany.
    2. Craig E. Landry & Alyson R. Lewis & Haiyong Liu & Hans Vogelsong, 2016. "Addressing Onsite Sampling in Analysis of Recreation Demand: Economic Value and Impact of Visitation to Cape Hatteras National Seashore," Marine Resource Economics, University of Chicago Press, vol. 31(3), pages 301-322.
    3. Domanski, Adam, 2009. "Estimating Mixed Logit Recreation Demand Models With Large Choice Sets," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49413, Agricultural and Applied Economics Association.
    4. Luís Cruz & Paula Simões & Eduardo Barata, 2014. "Combining Observed and Contingent Travel Behaviour: The Best of Both Worlds?," Notas Económicas, Faculty of Economics, University of Coimbra, issue 40, pages 7-25, December.
    5. Barbier, Edward B., 2012. "A spatial model of coastal ecosystem services," Ecological Economics, Elsevier, vol. 78(C), pages 70-79.
    6. Simões, Paula & Barata, Eduardo & Cruz, Luís, 2013. "Joint estimation using revealed and stated preference data: An application using a national forest," Journal of Forest Economics, Elsevier, vol. 19(3), pages 249-266.
    7. D. Matthew Massey & George R. Parsons, 2007. "State Dependence and Long Term Site Capital in a Random Utility Model of Recreation Demand," NCEE Working Paper Series 200711, National Center for Environmental Economics, U.S. Environmental Protection Agency, revised Dec 2007.

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