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OPTIMAL INVARIANT INFERENCE WHEN THE NUMBER OF INSTRUMENTS IS LARGE
Published online by Cambridge University Press: 01 June 2009
Abstract
This paper studies the asymptotic behavior of a Gaussian linear instrumental variables model in which the number of instruments diverges with the sample size. Asymptotic efficiency bounds are obtained for rotation invariant inference procedures and are shown to be attainable by procedures based on the limited information maximum likelihood estimator. The bounds are obtained by characterizing the limiting experiment associated with the model induced by the rotation invariance restriction.
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- Copyright © Cambridge University Press 2009
Footnotes
The authors thank Whitney Newey, Jim Powell, a co-editor, and a referee for helpful comments.
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