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Consistent noisy independent component analysis
[journal article]
Abstract We study linear factor models under the assumptions that factors are mutually independent and independent of errors, and errors can be correlated to some extent. Under factor non-Gaussianity, second to fourth-order moments are shown to yield full identification of the matrix of factor loadings. We d... view more
We study linear factor models under the assumptions that factors are mutually independent and independent of errors, and errors can be correlated to some extent. Under factor non-Gaussianity, second to fourth-order moments are shown to yield full identification of the matrix of factor loadings. We develop a simple algorithm to estimate the matrix of factor loadings from these moments. We run Monte Carlo simulations and apply our methodology to data on cognitive test scores, and financial data on stock returns.... view less
Keywords
factor analysis; linear model
Classification
Economic Statistics, Econometrics, Business Informatics
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords
Independent Component Analysis; Factor Analysis; High-order moments; Noisy ICA; JEL Classification: C14.
Document language
English
Publication Year
2009
Page/Pages
p. 12-25
Journal
Journal of Econometrics, 149 (2009) 1
DOI
https://doi.org/10.1016/j.jeconom.2008.12.019
Status
Postprint; peer reviewed
Licence
PEER Licence Agreement (applicable only to documents from PEER project)