Data Augmentation in Limited-Dependent Variable Models
This 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.
|Date of creation:|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: (0)1904 323776
Fax: (0)1904 323759
Web page: http://www.york.ac.uk/economics/
More information through EDIRC
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.:
- Nobile, Agostino, 2000. "Comment: Bayesian multinomial probit models with a normalization constraint," Journal of Econometrics, Elsevier, vol. 99(2), pages 335-345, December.
- Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, number 9780198773139, March.
- 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.
- 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.
When requesting a correction, please mention this item's handle: RePEc:yor:yorken:02/09. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Paul Hodgson)
If references are entirely missing, you can add them using this form.