Author(s)  
Gabriella Conti, Sylvia Fruehwirth-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.

JEL Codes  
C11: Bayesian Analysis: General
C38: Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Factor Models
C63: Computational Techniques; Simulation Modeling
Keywords  
bayesian factor modeling
exploratory factor analysis
identifiability
marginal data augmentation
model expansion
model selection