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Baysian Flexible Mixture Distribution Modelling of Dichotomous Choice Contingent Valuation with Heterogeneity

  • Jorge E. Arana
  • Carmelo J. Leon

This paper considers the performance of a model of mixture normal distributions for dichotomous choice contingent valuation data, which allows the researcher to consider unobserved heterogeneity across the sample. The model is flexible and approaches a semi-parametric model, since any empirical distribution can be represented by augmenting the number of mixture distributions. Bayesian inference allows for simple estimation of the model and is particularly appropriate for conducting inference with finite data sets. The proposed model is compared with other semi-parametric and parametric approaches using Monte Carlo simulation, under alternative assumptions regarding heteroscedasticity and heterogeneity in sample observations. It is found that the mixture normal model reduces bias and improves performance with respect to an alternative semi-parametric model, particularly when the sample is characterized by heterogeneous preferences.

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Paper provided by Econometric Society in its series Econometric Society 2004 North American Summer Meetings with number 568.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:nasm04:568
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  1. John F. Geweke & Michael P. Keane, 1997. "Mixture of normals probit models," Staff Report 237, Federal Reserve Bank of Minneapolis.
  2. Kajal Lahiri, 2005. "Analysis of Panel Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(4), pages 1093-1095.
  3. Mark Yuying An, 2000. "A Semiparametric Distribution for Willingness to Pay and Statistical Inference with Dichotomous Choice Contingent Valuation Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(3), pages 487-500.
  4. Creel, Michael & Loomis, John, 1997. "Semi-nonparametric Distribution-Free Dichotomous Choice Contingent Valuation," Journal of Environmental Economics and Management, Elsevier, vol. 32(3), pages 341-358, March.
  5. 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.
  6. Chib, Siddhartha, 1992. "Bayes inference in the Tobit censored regression model," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 79-99.
  7. Carson, Richard T & Wilks, Leanne & Imber, David, 1994. "Valuing the Preservation of Australia's Kakadu Conservation Zone," Oxford Economic Papers, Oxford University Press, vol. 46(0), pages 727-49, Supplemen.
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