Finite sample evidence of IV estimators under weak instruments

Alfonso Flores-Lagunes ()

Journal of Applied Econometrics, 2007, vol. 22, issue 3, pages 677-694

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. Copyright © 2007 John Wiley & Sons, Ltd.

View list of references View citations in EconPapers

Downloads: (external link)
http://hdl.handle.net/10.1002/jae.916 Link to full text; subscription required (text/html)
http://qed.econ.queensu.ca:80/jae/2007-v22.3/ Supporting data files and programs (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Ordering information: This journal article can be ordered from
http://www3.intersci ... e.jsp?issn=0883-7252

Access Statistics for this article

Journal of Applied Econometrics is edited by M. Hashem Pesaran

More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd.
Series data maintained by Christopher F. Baum ().