This article develops a framework for efficient IV estimators of random effects models with information in levels which can accomodate predetermined variables. Our formulation clarifies the relationship between the existing estimators and the role of transformations in panel data models. We characterise 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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Paper provided by Centre for Economic Performance, LSE in its series CEP Discussion Papers with number
07.
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