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Semiparametric estimation of the binary choice model for contingent valuation.

Research was conducted to attempt to estimate the binary choice model without imposing any parametric structure on the distribution of the stochastic term. The distribution-free maximum likelihood method developed by Stephen Cosslett (1983) is adapted for deriving the contingent value function with...

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Published in:Land Economics Vol. 72; pp. 462 - 474
Main Author: Li, Chuan-Zhong
Format: Article
Published: University of Wisconsin Press November 1996
Subjects:
Contingent valuation
Parameter estimation
Monte Carlo method
Natural resources
Valuation
Outdoor recreation
Economics
Forests & forestry
Sweden
Online Access:View this record in EBSCOhost
Summary:Research was conducted to attempt to estimate the binary choice model without imposing any parametric structure on the distribution of the stochastic term. The distribution-free maximum likelihood method developed by Stephen Cosslett (1983) is adapted for deriving the contingent value function with respect to observable exogenous variables. Monte Carlo comparisons with the probit estimates are presented, and the asymptotic consistency and relative efficiency of the approach are discussed. Data from a forest environment valuation survey are employed for empirical estimations.