Bayesian evaluation of a semi-parametric binary response model
AbstractIn this paper, we develop a Bayesian analysis of a semi-parametric binary choice model. The prior speciﬁcation of the functional parameter, namely the distribution function of a latent variable, is of the Dirichlet process type and the prior speciﬁcation of the Euclidean parameter, namely the coefficients of a linear combination of exogenous variables, is left arbitrary. The model identiﬁcation is ensured by ﬁxing the prior expectation of the functional parameter (see Mouchart et al. (1997)). Approximations for the posterior predictive distributions are obtained from two different sampling methods. Several questions are studied through an exploratory numerical analysis, such as the numerical convergence of the algorithms and of the methods and the general problem of contrasting semi-parametric and purely parametric speciﬁcation
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Bibliographic InfoPaper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 1998026.
Date of creation: 17 Mar 1998
Date of revision:
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Discrete choice model; semi-parametric model; Dirichlet process; Gibbs sampling; Bayesian speciﬁcation.;
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