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Generalized non-parametric deconvolution with an application to earnings dynamics

Bonhomme, S.; Robin, J.-M.; (2010) Generalized non-parametric deconvolution with an application to earnings dynamics. Review of Economic Studies , 77 (2) pp. 491-533. 10.1111/j.1467-937X.2009.00577.x. Green open access

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Abstract

In this paper, we construct a non-parametric estimator of the distributions of latent factors in linear independent multi-factor models under the assumption that factor loadings are known. Our approach allows estimation of the distributions of up to L(L+ 1)/2 factors given L measurements. The estimator uses empirical characteristic functions, like many available deconvolution estimators. 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 panel study of income dynamics (PSID) into permanent and transitory components.

Type: Article
Title: Generalized non-parametric deconvolution with an application to earnings dynamics
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/j.1467-937X.2009.00577.x
Publisher version: http://dx.doi.org/10.1111/j.1467-937X.2009.00577.x
Language: English
Additional information: The definitive version is available at www3.interscience.wiley.com
UCL classification: UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/17144
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