Bayesian Exploratory Factor Analysis

TY - JOUR

T1 - Bayesian Exploratory Factor Analysis

AU - Conti, Gabriella

AU - Frühwirth-Schnatter, Sylvia

AU - Heckman, James J.

AU - Piatek, Rémi

N1 - JEL classification: C11, C38, C63

PY - 2014/11/1

Y1 - 2014/11/1

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.

KW - Faculty of Social Sciences

KW - Bayesian Factor Models

KW - Exploratory Factor Analysis

KW - Identifiability

KW - Marginal Data Augmentation

KW - Model Selection

KW - Model Expansion

U2 - 10.1016/j.jeconom.2014.06.008

DO - 10.1016/j.jeconom.2014.06.008

M3 - Journal article

C2 - 25431517

SN - 0304-4076

VL - 183

SP - 31

EP - 57

JO - Journal of Econometrics

JF - Journal of Econometrics

IS - 1

ER -