Nonparametric estimation of the distribution function in contingent valuation models. (English) Zbl 1330.62428

Leslie, David S.; Kohn, Robert; Fiebig, Denzil G.

Bayesian Anal. 4, No. 3, 573-597 (2009).

Summary: 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 restricted distributions 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 variable selection and is illustrated using simulated and real data.

MSC:

62P20	Applications of statistics to economics
62G07	Density estimation
62H30	Classification and discrimination; cluster analysis (statistical aspects)
65C40	Numerical analysis or methods applied to Markov chains
62M10	Time series, auto-correlation, regression, etc. in statistics (GARCH)
91B84	Economic time series analysis

Keywords:

binary choice regression; Dirichlet process; latent variable; mixture model; variable selection