Panel data
Another look at the instrumental variable estimation of error-components models

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Abstract

This article develops a framework for efficient IV estimators of random effects models with information in levels which can accommodate predetermined variables. Our formulation clarifies the relationship between the existing estimators and the role of transformations in panel data models. We characterize the valid transformations for relevant models and show that optimal estimators are invariant to the transformation used to remove individual effects. We present an alternative transformation for models with predetermined instruments which preserves the orthogonality among the errors. Finally, we consider models with predetermined variables that have constant correlation with the effects and illustrate their importance with simulations.

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An earlier version of this paper was presented at the World Congress of the Econometric Society in Barcelona, August 1990, and at the Canadian Econometrics Study Group in Guelph, September 1990. It circulated as Centre for Economic Performance Discussion Paper No. 7 (London School of Economics, August 1990). We thank Steve Bond, David Card, Gary Chamberlain, Franco Peracchi, and specially Peter Schmidt for useful comments. All remaining errors are our own.

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