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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.

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  • 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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    2. 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.
    3. 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.
    4. 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.
    5. Craig E. Landry & John Bergstrom & John Salazar & Dylan Turner, 2021. "How Has the COVID‐19 Pandemic Affected Outdoor Recreation in the U.S.? A Revealed Preference Approach," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(1), pages 443-457, March.
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    7. von Haefen, Roger H. & Domanski, Adam, 2018. "Estimation and welfare analysis from mixed logit models with large choice sets," Journal of Environmental Economics and Management, Elsevier, vol. 90(C), pages 101-118.
    8. Jee W. Hwang & Chun Kuang & Okmyung Bin, 2019. "Are all Homeowners Willing to Pay for Better Schools? ─ Evidence from a Finite Mixture Model Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 58(4), pages 638-655, May.
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    10. Nobel, Anne & Lizin, Sebastien & Witters, Nele & Rineau, Francois & Malina, Robert, 2020. "The impact of wildfires on the recreational value of heathland: A discrete factor approach with adjustment for on-site sampling," Journal of Environmental Economics and Management, Elsevier, vol. 101(C).
    11. 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.
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    14. 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.

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