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Department of Statistics, UCLA

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A new identification condition for recursive models with correlated errors

Abstract

This article establishes a new criterion for the identification of recursive linear models in which some errors are correlated. We show that identification is ensured as long as error correlation does not exist between a cause and its direct}effect; no restrictions are imposed on errors associated with indirect causes.

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