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Parsimonious Estimation of the Covariance Matrix in Multinomial Probit Models


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  • Edward Cripps
  • Denzil Fiebig
  • Robert Kohn


This article presents a Bayesian analysis of a multinomial probit model by building on previous work that specified priors on identified parameters. The main contribution of our article is to propose a prior on the covariance matrix of the latent utilities that permits elements of the inverse of the covariance matrix to be identically zero. This allows a parsimonious representation of the covariance matrix when such parsimony exists. The methodology is applied to both simulated and real data, and its ability to obtain more efficient estimators of the covariance matrix and regression coefficients is assessed using simulated data.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 29 (2010)
Issue (Month): 2 ()
Pages: 146-157

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Handle: RePEc:taf:emetrv:v:29:y:2010:i:2:p:146-157

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Keywords: Bayesian analysis; Covariance selection; Data augmentation; Markov chain Monte Carlo; Multinomial probit;


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