Finite sample evidence of IV estimators under weak instruments

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25 Scopus citations

Abstract

We present finite sample evidence on different IV estimators available for linear models under weak instruments; explore the application of the bootstrap as a bias reduction technique to attenuate their finite sample bias; and employ three empirical applications to illustrate and provide insights into the relative performance of the estimators in practice. Our evidence indicates that the random-effects quasi-maximum likelihood estimator outperforms alternative estimators in terms of median point estimates and coverage rates, followed by the bootstrap bias-corrected version of LIML and LIML. However, our results also confirm the difficulty of obtaining reliable point estimates in models with weak identification and moderate-size samples.

Original languageEnglish (US)
Pages (from-to)677-694
Number of pages18
JournalJournal of Applied Econometrics
Volume22
Issue number3
DOIs
StatePublished - Apr 1 2007
Externally publishedYes

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics

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