TY - JOUR
T1 - Bayesian exploratory factor analysis
AU - Conti, Gabriella
AU - Frühwirth-Schnatter, Sylvia
AU - Heckman, James J.
AU - Piatek, Rémi
PY - 2014
Y1 - 2014
N2 - 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.
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.
UR - http://www.sciencedirect.com/science/article/pii/S0304407614001493
U2 - 10.1016/j.jeconom.2014.06.008
DO - 10.1016/j.jeconom.2014.06.008
M3 - Journal article
SN - 0304-4076
VL - 183
SP - 31
EP - 57
JO - Journal of Econometrics
JF - Journal of Econometrics
IS - 1
ER -