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Optimal Invariant Inference When The Number Of Instruments Is Large

  • Chioda, Laura
  • Jansson, Michael

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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Article provided by Cambridge University Press in its journal Econometric Theory.

Volume (Year): 25 (2009)
Issue (Month): 03 (June)
Pages: 793-805

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Handle: RePEc:cup:etheor:v:25:y:2009:i:03:p:793-805_09
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