Bayesian Exploratory Factor Analysis

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Bayesian Exploratory Factor Analysis. / Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.; Piatek, Rémi.

In: Journal of Econometrics, Vol. 183, No. 1, 11.2014, p. 31-57.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Conti, G, Frühwirth-Schnatter, S, Heckman, JJ & Piatek, R 2014, 'Bayesian Exploratory Factor Analysis', Journal of Econometrics, vol. 183, no. 1, pp. 31-57. https://doi.org/10.1016/j.jeconom.2014.06.008

APA

Conti, G., Frühwirth-Schnatter, S., Heckman, J. J., & Piatek, R. (2014). Bayesian Exploratory Factor Analysis. Journal of Econometrics, 183(1), 31-57. https://doi.org/10.1016/j.jeconom.2014.06.008

Vancouver

Conti G, Frühwirth-Schnatter S, Heckman JJ, Piatek R. Bayesian Exploratory Factor Analysis. Journal of Econometrics. 2014 Nov;183(1):31-57. https://doi.org/10.1016/j.jeconom.2014.06.008

Author

Conti, Gabriella ; Frühwirth-Schnatter, Sylvia ; Heckman, James J. ; Piatek, Rémi. / Bayesian Exploratory Factor Analysis. In: Journal of Econometrics. 2014 ; Vol. 183, No. 1. pp. 31-57.

Bibtex

@article{9b143197c6194ecfa74386c9eb587db1,
title = "Bayesian Exploratory Factor Analysis",
abstract = "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.",
keywords = "Faculty of Social Sciences, Bayesian Factor Models, Exploratory Factor Analysis, Identifiability, Marginal Data Augmentation, Model Selection, Model Expansion",
author = "Gabriella Conti and Sylvia Fr{\"u}hwirth-Schnatter and Heckman, {James J.} and R{\'e}mi Piatek",
note = "JEL classification: C11, C38, C63",
year = "2014",
month = nov,
doi = "10.1016/j.jeconom.2014.06.008",
language = "English",
volume = "183",
pages = "31--57",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier",
number = "1",

}

RIS

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

Y1 - 2014/11

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

VL - 183

SP - 31

EP - 57

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 1

ER -

ID: 82258565