<p><p><p><p>In this paper,we construct a nonparametric estimator of the distributions of latent factors in linear independent multi-factor models under the assumption that factor loadings are known. Our approach allows to estimate the distributions of up to L(L+1)/2 factors given L measurements. The estimator works through empirical characteristic functions. We show that it is consistent, and derive asymptotic convergence rates. Monte-Carlo simulations show good finite-sample performance, less so if distributions are highly skewed or leptokurtic. We finally apply the generalized deconvolution procedure to decompose individual log earnings from the PSID into permanent and transitory components.</p></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.2008.0308
- Publisher
- IFS
Suggested citation
Bonhomme, S and Robin, J. (2008). Generalized nonparametric deconvolution with an application to earnings dynamics. London: IFS. Available at: https://ifs.org.uk/publications/generalized-nonparametric-deconvolution-application-earnings-dynamics (accessed: 1 July 2024).
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