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 coefficients fixed. A method for flexibly estimating the link function 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 restricteddistributions in earlier papers. The model is estimated using a novel MCMC sampling scheme that avoids the high autocorrelations in the iterates that usually arise when sampling latent variables that are mixtures. The method allows for variableselection and is illustrated using simulated and real data.
|Translated title of the contribution||Nonparametric estimation of he distribution function in contingent valuation models|
|Pages (from-to)||573 - 598|
|Number of pages||26|
|Publication status||Published - Jul 2009|