TY - JOUR

T1 - Nonparametric estimation of the distribution function in contingent valuation models

AU - Leslie, David S.

AU - Kohn, Robert

AU - Fiebig, Denzil G.

PY - 2009/9/1

Y1 - 2009/9/1

N2 - Contingent valuation models are used in Economics to value non-market goods and can be expressed as binary choice regression models with oneof 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 mixtureprior on the space of all latent variable distributions, instead of the more restricteddistributions in earlier papers. The model is estimated using a novel MCMC sam-pling scheme that avoids the high autocorrelations in the iterates that usually arisewhen sampling latent variables that are mixtures. The method allows for variableselection and is illustrated using simulated and real data.

AB - Contingent valuation models are used in Economics to value non-market goods and can be expressed as binary choice regression models with oneof 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 mixtureprior on the space of all latent variable distributions, instead of the more restricteddistributions in earlier papers. The model is estimated using a novel MCMC sam-pling scheme that avoids the high autocorrelations in the iterates that usually arisewhen sampling latent variables that are mixtures. The method allows for variableselection and is illustrated using simulated and real data.

KW - binary choice regression

KW - Dirichlet process

KW - latent variable

KW - mixture model

KW - variable selection

U2 - 10.1214/09-BA421

DO - 10.1214/09-BA421

M3 - Journal article

VL - 4

SP - 573

EP - 598

JO - Bayesian Analysis

JF - Bayesian Analysis

SN - 1936-0975

IS - 3

ER -