Baysian Flexible Mixture Distribution Modelling of Dichotomous Choice Contingent Valuation with Heterogeneity

Jorge E. Araña () and Carmelo J. Leon

No 568, Econometric Society 2004 North American Summer Meetings from Econometric Society

Abstract: This paper considers the performance of a model of mixture normal distributions for dichotomous choice contingent valuation data, which allows the researcher to consider unobserved heterogeneity across the sample. The model is flexible and approaches a semi-parametric model, since any empirical distribution can be represented by augmenting the number of mixture distributions. Bayesian inference allows for simple estimation of the model and is particularly appropriate for conducting inference with finite data sets. The proposed model is compared with other semi-parametric and parametric approaches using Monte Carlo simulation, under alternative assumptions regarding heteroscedasticity and heterogeneity in sample observations. It is found that the mixture normal model reduces bias and improves performance with respect to an alternative semi-parametric model, particularly when the sample is characterized by heterogeneous preferences.

Keywords: Bayesian Econometrics; Mixture of Normals; Choice Experiments (search for similar items in EconPapers)
JEL-codes: C11 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dcm and nep-ecm
Date: 2004-08-11
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
http://repec.org/esNASM04/up.1844.1075598983.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:ecm:nasm04:568

Access Statistics for this paper

More papers in Econometric Society 2004 North American Summer Meetings from Econometric Society
Contact information at EDIRC.
Series data maintained by Christopher F. Baum ().