Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/79374 
Year of Publication: 
2007
Series/Report no.: 
cemmap working paper No. CWP04/08
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
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 develop a simple algorithm to estimate the matrix of factor loadings from these moments. We run Monte Carlo simulations and apply our methodology to British data on cognitive test scores
Subjects: 
Independant Component Analysis , Factor Analysis , high order moments , noisy ICA
JEL: 
C14
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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