The case against JIVE
通过大量蒙特卡洛实验比较JIVE与2SLS、LIML估计量，发现JIVE总是比2SLS更分散，且几乎在所有方面都不如LIML，尤其在弱工具变量时表现更差，因此不推荐使用。
Abstract We perform an extensive series of Monte Carlo experiments to compare the performance of two variants of the ‘jackknife instrumental variables estimator’, or JIVE, with that of the more familiar 2SLS and LIML estimators. We find no evidence to suggest that JIVE should ever be used. It is always more dispersed than 2SLS, often very much so, and it is almost always inferior to LIML in all respects. Interestingly, JIVE seems to perform particularly badly when the instruments are weak. Copyright © 2006 John Wiley & Sons, Ltd.