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Generalized nonparametric deconvolution with an application to earnings dynamics

Authors: Stéphane Bonhomme and Jean-Marc Robin
Date: 01 April 2010
Type: Journal Article, Review of Economic Studies, Vol. 77, Issue 2, pp. 491-533
DOI: 10.1111/j.1467-937X.2009.00577.x

Abstract

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.

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Stéphane Bonhomme and Jean-Marc Robin February 2008, Generalized nonparametric deconvolution with an application to earnings dynamics, cemmap Working Paper, CWP03/08

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