The directional identification problem in Bayesian factor analysis: An ex-post approach
Jens Boysen-Hogrefe () and
No 1799, Kiel Working Papers from Kiel Institute for the World Economy (IfW)
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.
Keywords: Bayesian Estimation; Factor Models; Multimodality; Ordering; Orthogonal Transformation (search for similar items in EconPapers)
JEL-codes: C11 C31 C38 C51 C52 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Working Paper: The Directional Identification Problem in Bayesian Factor Analysis: An Ex-Post Approach (2013)
Working Paper: The directional identification problem in Bayesian factor analysis: An ex-post approach (2012)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:zbw:ifwkwp:1799
Access Statistics for this paper
More papers in Kiel Working Papers from Kiel Institute for the World Economy (IfW) Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().