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Modelling contingent valuation iterated elicitation data with an MCMC approach

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  • Jorge E. Araña

    ()
    (Universidad de Las Palmas de Gran Canaria)

  • Carmelo J. Léon

    ()
    (Universidad de Las Palmas de Gran Canaria)

Abstract

The valuation of non-market goods involves iterated elicitation questions which obtain more information from the sample respondents and lead to more efficient welfare estimates. In this paper we consider the improvements which could be obtained by utilising a Bayesian MCMC approach to model this type of data. A fully informative prior resulting from previous stages is compared with a flat non-informative prior utilising both simulated and empirical data. These priors are combined with data in each stage to form the posteriors which are simulated with Gibbs sampling algorithms. The models are applied to an elicitation tree involving two successive dichotomous choice questions followed by an open-ended question. Monte Carlo simulations show that taking into account the information process implicit in successive elicitation improves the performance of the results at each stage and increases efficiency. Thus, the model allows the researcher to consider the evolving process along the elicitation tree, while increasing useful information obtained from the individual.

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Bibliographic Info

Article provided by IEF in its journal Hacienda Pública Española/Revista de Economía Pública.

Volume (Year): 177 (2006)
Issue (Month): 2 (April)
Pages: 83-105

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Handle: RePEc:hpe:journl:y:2006:v:177:i:2:p:83-105

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Related research

Keywords: Bayesian methods; Contingent valuation; Gibbs sampling; Iterated Elicitation; National Parks.;

References

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  1. Herriges, Joseph A. & Shogren, Jason F., 1996. "Starting Point Bias in Dichotomous Choice Valuation with Follow-Up Questioning," Staff General Research Papers 1501, Iowa State University, Department of Economics.
  2. C. J. Leon & F. J. Vazquez-Polo & N. Guerra & P. Riera, 2002. "A Bayesian model for benefit transfer: application to national parks in Spain," Applied Economics, Taylor & Francis Journals, vol. 34(6), pages 749-757.
  3. Cameron Trudy Ann & Quiggin John, 1994. "Estimation Using Contingent Valuation Data from a Dichotomous Choice with Follow-Up Questionnaire," Journal of Environmental Economics and Management, Elsevier, vol. 27(3), pages 218-234, November.
  4. Cameron, Trudy Ann, 1988. "A new paradigm for valuing non-market goods using referendum data: Maximum likelihood estimation by censored logistic regression," Journal of Environmental Economics and Management, Elsevier, vol. 15(3), pages 355-379, September.
  5. Bateman, Ian J. & Langford, Ian H. & Jones, Andrew P. & Kerr, Geoffrey N., 2001. "Bound and path effects in double and triple bounded dichotomous choice contingent valuation," Resource and Energy Economics, Elsevier, vol. 23(3), pages 191-213, July.
  6. Cooper Joseph C., 1993. "Optimal Bid Selection for Dichotomous Choice Contingent Valuation Surveys," Journal of Environmental Economics and Management, Elsevier, vol. 24(1), pages 25-40, January.
  7. Arana, Jorge E. & Leon, Carmelo J., 2005. "Flexible mixture distribution modeling of dichotomous choice contingent valuation with heterogenity," Journal of Environmental Economics and Management, Elsevier, vol. 50(1), pages 170-188, July.
  8. Ian Langford & Ian Bateman & Hugh Langford, 1996. "A multilevel modelling approach to triple-bounded dichotomous choice contingent valuation," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 7(3), pages 197-211, April.
  9. Chib, Siddhartha, 1992. "Bayes inference in the Tobit censored regression model," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 79-99.
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