Modified Instruments: An Iterative Process for Reducing Endogeneity Bias Using Large Samples

This Article proposes an iterative process to reduce bias of the instrumental variable estimator using large samples. One can achieve bias reduction by modifying the data of an instrument. Success in reducing bias depends on the magnitude of exogeneity in the instrument. The common exogeneity between the instrument and the problem variable must be greater than the common endogeneity between the same variables. If the instrument is weakly exogenous, the iterative process will not affect the estimates. Empirically, the iterative process requires searching for parameters that allow minimizing bias. This paper presents a convergence algorithm to search for parameters. The search algorithm and statistical inference are based on bootstrapping techniques. The results are presented in a simulation context.