<p><p><p>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.</p></p></p>
Authors
Research Fellow Centre for Monetary and Financial Studies (CEMFI)
Manuel is a Research Fellow of the IFS and a Professor of Econometrics at CEMFI, Madrid.
Professor of Economics University of Chicago
Working Paper details
- DOI
- 10.1920/wp.cem.2009.2209
- Publisher
- IFS
Suggested citation
Arellano, M and Bonhomme, S. (2009). Identifying distributional characteristics in random coefficients panel data models. London: IFS. Available at: https://ifs.org.uk/publications/identifying-distributional-characteristics-random-coefficients-panel-data-models (accessed: 15 May 2024).
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