Generalized nonparametric deconvolution with an application to earnings dynamics - Published Review of Economic Studies, Vol. 77, Issue 2, pp. 491-533, 2010
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1 octobre 2008
- handle: 10670/1.a4wufe
- hal: hal-03459860
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C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General/C.C1.C13 - Estimation: General C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General/C.C1.C14 - Semiparametric and Nonparametric Methods: GeneralSujets proches
Earnings Wage rates Wage-fund Departmental salaries Remuneration Pay Salaries Compensation Working class--Wages Statistical data Statistical analysis Statistical methods Statistical science Philosophy--Methodology Factors Agents, Manufacturers' Manufacturers' representatives FactorsCiter ce document
Stéphane Bonhomme et al., « Generalized nonparametric deconvolution with an application to earnings dynamics - Published Review of Economic Studies, Vol. 77, Issue 2, pp. 491-533, 2010 », Archive ouverte de Sciences Po (SPIRE), ID : 10670/1.a4wufe
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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.