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Nonparametric estimation of the distribution function in contingent valuation models

Research output: Contribution to journalJournal articlepeer-review

<mark>Journal publication date</mark>1/09/2009
<mark>Journal</mark>Bayesian Analysis
Issue number3
Number of pages15
Pages (from-to)573-598
Publication StatusPublished
<mark>Original language</mark>English


Contingent valuation models are used in Economics to value non-
market goods and can be expressed as binary choice regression models with one
of the regression coe±cients ¯xed. A method for °exibly estimating the link func-
tion of such binary choice model is proposed by using a Dirichlet process mixture
prior on the space of all latent variable distributions, instead of the more restricted
distributions in earlier papers. The model is estimated using a novel MCMC sam-
pling scheme that avoids the high autocorrelations in the iterates that usually arise
when sampling latent variables that are mixtures. The method allows for variable
selection and is illustrated using simulated and real data.