The directional identification problem in Bayesian factor analysis: An ex-post approach
AbstractDue 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. --
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Bibliographic InfoPaper provided by Christian-Albrechts-University of Kiel, Department of Economics in its series Economics Working Papers with number 2012-11.
Date of creation: 2012
Date of revision:
Bayesian Estimation; Factor Models; Multimodality; Ordering Problem; Orthogonal Transformation;
Other versions of this item:
- Pape, Markus & Aßmann, Christian & Boysen-Hogrefe, Jens, 2013. "The Directional Identification Problem in Bayesian Factor Analysis: An Ex-Post Approach," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79990, Verein für Socialpolitik / German Economic Association.
- Christian Aßmann & Jens Boysen-Hogrefe & Markus Pape, 2012. "The Directional Identification Problem in Bayesian Factor Analysis: An Ex-Post Approach," Kiel Working Papers 1799, Kiel Institute for the World Economy.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
- C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-11-11 (All new papers)
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.
- Jean Boivin & Serena Ng, 2003.
"Are More Data Always Better for Factor Analysis?,"
NBER Working Papers
9829, National Bureau of Economic Research, Inc.
- Donald Rubin & Dorothy Thayer, 1982. "EM algorithms for ML factor analysis," Psychometrika, Springer, vol. 47(1), pages 69-76, March.
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