Bayesian Exploratory Factor Analysis

Gabriella Conti, Sylvia Frühwirth-Schnatter, James Heckman and Rémi Piatek

No 2014-08, NRN working papers from The Austrian Center for Labor Economics and the Analysis of the Welfare State, Johannes Kepler University Linz, Austria

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: Bayesian Factor Models; Exploratory Factor Analysis; Identifiability; Marginal Data Augmentation; Model Expansion; Model Selection. (search for similar items in EconPapers)
JEL-codes: C11 C38 C63 (search for similar items in EconPapers)
Pages: 73 pages
Date: 2014-06
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (45)

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http://www.labornrn.at/wp/2014/wp1408.pdf (application/pdf)

Related works:
Journal Article: Bayesian exploratory factor analysis (2014)
Working Paper: Bayesian exploratory factor analysis (2014)
Working Paper: Bayesian Exploratory Factor Analysis (2014)
Working Paper: Bayesian exploratory factor analysis (2014)
Working Paper: Bayesian Exploratory Factor Analysis (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:jku:nrnwps:2014_08

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