Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/64789 
Year of Publication: 
2009
Series/Report no.: 
cemmap working paper No. CWP22/09
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
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.
Subjects: 
panel data
random coefficients
multiple effects
nonparametric identification
JEL: 
C23
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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