Generalized Non-Parametric Deconvolution with an Application to Earnings Dynamics

• 成果类型: Article
• 署名作者: Bonhomme, Stephane; Robin, Jean-Marc
• 署名单位: Paris School of Economics; Universite Paris Cite; University of London; University College London
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1111/j.1467-937X.2009.00577.x
• 发表日期: 2010
• 页码: 491-533
• 关键词: optimal rates; panel data; models; income; CONVERGENCE; regression; variables

摘要

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.