Flores-Lagunes article on finite sample evidence of IV estimators published in JAE
Mar 31, 2017
Journal of Applied Econometrics, March 2007
The author presents finite sample evidence on different IV estimators available for linear models under weak instruments; explores the application of the bootstrap as a bias reduction technique to attenuate their finite sample bias; and employs three empirical applications to illustrate and provide insights into the relative performance of the estimators in practice. The author's 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, the author's results also confirm the difficulty of obtaining reliable point estimates in models with weak identification and moderate-size samples.