Generalized Non-Parametric Deconvolution with an Application to Earnings Dynamics

Stéphane Bonhomme and Jean-Marc Robin

The Review of Economic Studies, 2010, vol. 77, issue 2, 491-533

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. Copyright , Wiley-Blackwell.

Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (94)

Downloads: (external link)
http://hdl.handle.net/10.1111/j.1467-937X.2009.00577.x (application/pdf)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Generalized nonparametric deconvolution with an application to earnings dynamics (2008)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:restud:v:77:y:2010:i:2:p:491-533

Access Statistics for this article

The Review of Economic Studies is currently edited by Thomas Chaney, Xavier d’Haultfoeuille, Andrea Galeotti, Bård Harstad, Nir Jaimovich, Katrine Loken, Elias Papaioannou, Vincent Sterk and Noam Yuchtman

More articles in The Review of Economic Studies from Review of Economic Studies Ltd
Bibliographic data for series maintained by Oxford University Press ().