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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Department of Economics, University of York in its series Discussion Papers with number 02/09.
Date of creation:
Date of revision:
Contact details of provider:
Postal: Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom
Phone: (0)1904 323776
Fax: (0)1904 323759
Web page: http://www.york.ac.uk/economics/
More information through EDIRC
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.:
- 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.
- 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.
- Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, number 9780198773139.
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.