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Simulated Maximum Likelihood For Double-Bounded Referendum Models

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  • Riddel, Mary C.

Abstract

Although joint estimation of referendum-type contingent value (CV) survey responses using maximum-likelihood models is preferred to single-equation estimation, it has been largely disregarded because estimation involves evaluating multivariate normal probabilities. New developments in the construction of probability simulators have addressed this problem, and simulated maximum likelihood (SML) for multiple-good models is now possible. This analysis applies SML for a three-good model under a double-bounded questioning format. Results indicate joint estimation substantially improves the variances of the parameters and willingness-to-pay estimates.

Suggested Citation

  • Riddel, Mary C., 2001. "Simulated Maximum Likelihood For Double-Bounded Referendum Models," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 26(2), pages 1-17, December.
    • Handle: RePEc:ags:jlaare:31040
    DOI: 10.22004/ag.econ.31040
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    References listed on IDEAS

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    2. Mary Riddel & John Loomis, 1998. "Joint Estimation of Multiple CVM Scenarios under a Double Bounded Questioning Format," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 12(1), pages 77-98, July.
    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.
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    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. Geweke, John & Keane, Michael P & Runkle, David, 1994. "Alternative Computational Approaches to Inference in the Multinomial Probit Model," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 609-632, November.
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