In this paper we consider parameter estimation in a linear simultaneous equations model. It is well known that two stage least squares (2SLS) estimators may perform poorly when the instruments are weak. In this case 2SLS tends to suffer from substantial small sample biases. It is also known that LIML and Nagar-type estimators are less biased than 2SLS but suffer from large small sample variability. We construct a bias corrected version of 2SLS based on the Jackknife principle.