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Confirmatory factor analyses on non–normal panel data: an application to banking

Confirmatory factor analyses on non–normal panel data: an application to banking Factor analysis of multivariate longitudinal data are discussed, where measurements are taken from individuals at several occasions. Unbalanced cases, in which some individuals do not appear at all occasions and the number of measured individuals may change from one occasion to another, are considered. For such cases, the full likelihood method is difficult even if a particular distribution is assumed. In this paper, a relatively simple method based on a partial likelihood is considered, and is shown to have various advantages over the full likelihood method and the time–series modelling. It is shown that the associated inference procedures, including the goodness–of–fit statistic, have a good asymptotic performance for a broad class of non–normal data having any time trend. The proposed method is compared with standard methods using real data from the banking sector. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Computational Economics and Econometrics Inderscience Publishers

Confirmatory factor analyses on non–normal panel data: an application to banking

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1757-1170
eISSN
1757-1189
DOI
10.1504/IJCEE.2013.058492
Publisher site
See Article on Publisher Site

Abstract

Factor analysis of multivariate longitudinal data are discussed, where measurements are taken from individuals at several occasions. Unbalanced cases, in which some individuals do not appear at all occasions and the number of measured individuals may change from one occasion to another, are considered. For such cases, the full likelihood method is difficult even if a particular distribution is assumed. In this paper, a relatively simple method based on a partial likelihood is considered, and is shown to have various advantages over the full likelihood method and the time–series modelling. It is shown that the associated inference procedures, including the goodness–of–fit statistic, have a good asymptotic performance for a broad class of non–normal data having any time trend. The proposed method is compared with standard methods using real data from the banking sector.

Journal

International Journal of Computational Economics and EconometricsInderscience Publishers

Published: Jan 1, 2013

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