Data Augmentation in Limited-Dependent Variable Models
AbstractThis paper proposes a scheme that speeds up the convergence of Markov Chain Monte Carlo (MCMC) algorithms in the context of limited-dependent variable models. The algorithm reduces autocorrelations more than the recently proposed Parameter Expansion Data Augumentation (PX-DA) algorithm. In addition, the paper provides an algorithm to sample a variance-covariance matrix with restrictions directly from the conditional posterior distribution. Finally, it is shown that the PX-DA algorithm, as applied to the multivariate probit model, can be seen as sampling from a different parameterization of the model. However, in some cases the PX-DA algorithm is not invariant to reparameterizations, and a slightly different algorithm is proposed.
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Bibliographic InfoPaper provided by Department of Economics, University of York in its series Discussion Papers with number 02/09.
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Postal: Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom
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data augmentation; parameter-expansion-data-augmentation; inverted wishart; multivariate probit; reparameterization.;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2003-02-03 (All new papers)
- NEP-DCM-2003-02-03 (Discrete Choice Models)
- NEP-ECM-2003-02-10 (Econometrics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Amit, Yali, 1991. "On rates of convergence of stochastic relaxation for Gaussian and non-Gaussian distributions," Journal of Multivariate Analysis, Elsevier, vol. 38(1), pages 82-99, July.
- Nobile, Agostino, 2000. "Comment: Bayesian multinomial probit models with a normalization constraint," Journal of Econometrics, Elsevier, vol. 99(2), pages 335-345, December.
- McCulloch, Robert E. & Polson, Nicholas G. & Rossi, Peter E., 2000. "A Bayesian analysis of the multinomial probit model with fully identified parameters," Journal of Econometrics, Elsevier, vol. 99(1), pages 173-193, November.
- Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, number 9780198773139, September.
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