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Autor:innen: 
Conti, Gabriella
Frühwirth-Schnatter, Sylvia
Heckman, James
Piatek, Rémi
Datum: 
2014
Schriftenreihe/Nr.: 
cemmap working paper No. CWP30/14
Verlag: 
Centre for Microdata Methods and Practice (cemmap), London
Zusammenfassung: 
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.
Schlagwörter: 
Bayesian Factor Models
Exploratory Factor Analysis
Identifiability
Marginal Data Augmentation
Model Expansion
Model Selection
JEL: 
C11
C38
C63
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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