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Identifying distributional characteristics in random coefficients panel data models

  • Manuel Arellano


    (Institute for Fiscal Studies and CEMFI)

  • Stéphane Bonhomme


    (Institute for Fiscal Studies and University of Chicago)

We study the identification of panel models with linear individual-specific coefficients, when T is fixed. We show identification of the variance of the effects under conditional uncorrelatedness. Identification requires restricted dependence of errors, reflecting a trade-off between heterogeneity and error dynamics. We show identification of the density of individual effects when errors follow an ARMA process under conditional independence. We discuss GMM estimation of moments of effects and errors, and introduce a simple density estimator of a slope effect in a special case. As an application we estimate the effect that a mother smokes during pregnancy on child's birth weight.

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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP22/09.

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Date of creation: Aug 2009
Date of revision:
Handle: RePEc:ifs:cemmap:22/09
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