Bayesian Exploratory Factor Analysis

Research output: Contribution to journalJournal articlepeer-review

  • Gabriella Conti
  • Sylvia Frühwirth-Schnatter
  • James J. Heckman
  • Rémi Piatek
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high-dimensional set of psychological measurements.
Original languageEnglish
JournalJournal of Econometrics
Volume183
Issue number1
Pages (from-to)31-57
Number of pages27
ISSN0304-4076
DOIs
Publication statusPublished - Nov 2014

Bibliographical note

JEL classification: C11, C38, C63

    Research areas

  • Faculty of Social Sciences - Bayesian Factor Models, Exploratory Factor Analysis, Identifiability, Marginal Data Augmentation, Model Selection, Model Expansion

ID: 82258565