TY  - RPRT
        AU  - Conti, Gabriella
AU  - Frühwirth-Schnatter, Sylvia
AU  - Heckman, James J.
AU  - Piatek, Rémi
        TI  - Bayesian Exploratory Factor Analysis
        PY  - 2014/Jul/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 8338
        UR  - https://www.iza.org/publications/dp8338
        AB  - 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.
        KW  - marginal data augmentation
KW  - identifiability
KW  - exploratory factor analysis
KW  - Bayesian factor models
KW  - model expansion
KW  - model selection
        ER  -