Nonparametric estimation of the distribution function in contingent valuation models

David S. Leslie, R Kohn, DG Fiebig

Research output: Contribution to journalArticle (Academic Journal)peer-review

2 Citations (Scopus)

Abstract

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 contributionNonparametric estimation of he distribution function in contingent valuation models
Original languageEnglish
Pages (from-to)573 - 598
Number of pages26
JournalBayesian Analysis
Volume4
Issue number3
DOIs
Publication statusPublished - Jul 2009

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