The Directional Identification Problem in Bayesian Factor Analysis: An Ex-Post Approach
Due to their well-known indeterminacies, factor models require identifying assumptions to guarantee unique parameter estimates. For Bayesian estimation, these identifying assumptions are usually implemented by imposing constraints on certain model parameters. This strategy, however, may result in posterior distributions with shapes that depend on the ordering of cross-sections in the data set. We propose an alternative approach, which relies on a sampler without the usual identifying constraints. Identification is reached ex-post based on a Procrustes transformation. Resulting posterior estimates are ordering invariant and show favorable properties with respect to convergence and statistical as well as numerical accuracy
|Date of creation:||Oct 2012|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: +49 431 8814-1
Fax: +49 431 85853
Web page: http://www.ifw-kiel.de
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.:
- Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
- Otrok, C. & Whiteman, C.H., 1996.
"Bayesian Leading Indicators: Measuring and Predicting Economic Conditions in Iowa,"
96-14, University of Iowa, Department of Economics.
- Otrok, Christopher & Whiteman, Charles H, 1998. "Bayesian Leading Indicators: Measuring and Predicting Economic Conditions in Iowa," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 997-1014, November.
- Boivin, Jean & Ng, Serena, 2006.
"Are more data always better for factor analysis?,"
Journal of Econometrics,
Elsevier, vol. 132(1), pages 169-194, May.
- Donald Rubin & Dorothy Thayer, 1982. "EM algorithms for ML factor analysis," Psychometrika, Springer, vol. 47(1), pages 69-76, March.
When requesting a correction, please mention this item's handle: RePEc:kie:kieliw:1799. 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: (Dieter Stribny)
If references are entirely missing, you can add them using this form.