The directional identification problem in Bayesian factor analysis: An ex-post approach

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Kiel Working Paper ; No. 1799

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Bayesian Analysis: General ; Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models ; Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models ; Model Construction and Estimation ; Model Evaluation, Validation, and Selection
• Thema: Bayesian Estimation ; Factor Models ; Multimodality ; Ordering ; Orthogonal Transformation

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Aßmann, Christian ; Boysen-Hogrefe, Jens ; Pape, Markus
• Ereignis: Veröffentlichung
• (wer): Kiel Institute for the World Economy (IfW)
• (wo): Kiel
• (wann): 2012

Weitere Informationen

• Handle: http://hdl.handle.net/10419/67349
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ