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. Identi cation 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 Verein für Socialpolitik / German Economic Association in its series Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order with number 79990.
Date of creation: 2013
Date of revision:
Other versions of this item:
- Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2012. "The directional identification problem in Bayesian factor analysis: An ex-post approach," Economics Working Papers 2012-11, Christian-Albrechts-University of Kiel, Department of Economics.
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-02-02 (All new papers)
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