<p><p><p>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.</p></p></p>
Authors
![Jean-Marc Robin](/sites/default/files/styles/square_desktop/public/2022-07/Jean-Marc%20Robin.jpg?itok=umZw1Ufv)
Research Fellow Sciences Po and University College London
Jean-Marc is a Research Fellow of the IFS and a Professor of Economics at Sciences Po, Paris, and University College London.
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Professor of Economics University of Chicago
Working Paper details
- DOI
- 10.1920/wp.cem.2008.0408
- Publisher
- IFS
Suggested citation
Bonhomme, S and Robin, J. (2008). Consistent noisy independent component analysis. London: IFS. Available at: https://ifs.org.uk/publications/consistent-noisy-independent-component-analysis (accessed: 1 July 2024).
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