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Estimating a Censored Dynamic Panel Data Model With an Application to Earnings Dynamics

Author

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  • Luojia Hu

    (Princeton University)

Abstract

This paper proposes a method for estimating a censored panel data model with a lagged latent dependent variable and individual-specific fixed effects. The main insight is to trim observations in such a way that a certain symmetry, which was destroyed by censoring, is restored. Based on the restored symmetry, orthogonality conditions are constructed and GMM estimation is implemented. The estimation method is used to study earnings dynamics, using matched data from the Current Population Survey and Social Security Administration (CPS- SSA) Earnings Record for a sample of men who were born in 1930-39 and living in the South dining the period of 1957-73. The SSA earnings are top-coded at the maximum social security taxable level. Although linear GMM estimation yields no difference in earnings dynamics by race, the earnings process for white men appears to be more persistent than that for black men (conditional on individual heterogeneity) after censoring is taken into account.

Suggested Citation

  • Luojia Hu, 2000. "Estimating a Censored Dynamic Panel Data Model With an Application to Earnings Dynamics," Working Papers 814, Princeton University, Department of Economics, Industrial Relations Section..
    • Handle: RePEc:pri:indrel:435
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    Cited by:

    1. Shiu, Ji-Liang & Hu, Yingyao, 2013. "Identification and estimation of nonlinear dynamic panel data models with unobserved covariates," Journal of Econometrics, Elsevier, vol. 175(2), pages 116-131.

    More about this item

    Keywords

    panel data; censored regression; earnings dynamics;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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