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Generalized nonparametric deconvolution with an application to earnings dynamics - Published Review of Economic Studies, Vol. 77, Issue 2, pp. 491-533, 2010

Author

Listed:
  • Stéphane Bonhomme
  • Jean-Marc Robin

    (Economics Department (UCL))

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.

Suggested Citation

  • Stéphane Bonhomme & Jean-Marc Robin, 2008. "Generalized nonparametric deconvolution with an application to earnings dynamics - Published Review of Economic Studies, Vol. 77, Issue 2, pp. 491-533, 2010," Sciences Po publications info:hdl:2441/eu4vqp9ompq, Sciences Po.
    • Handle: RePEc:spo:wpmain:info:hdl:2441/eu4vqp9ompqllr09j004pc20k
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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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