Consistency without Inference: Instrumental Variables in Practical Application

I use Monte Carlo simulations, the jackknife and multiple forms of the bootstrap to study a comprehensive sample of 1359 instrumental variables regressions in 31 papers published in the journals of the American Economic Association. Monte Carlo simulations based upon published regressions show that non-iid error processes adversely affect the size and power of IV estimates, while increasing the bias of IV relative to OLS, producing a very low ratio of power to size and mean squared error that is almost always larger than biased OLS. Weak instrument pre-tests based upon F-statistics are found to be largely uninformative of both size and bias. In published papers, statistically significant IV results generally depend upon only one or two observations or clusters, excluded instruments often appear to be irrelevant, there is little statistical evidence that OLS is biased, and IV confidence intervals almost always include OLS point estimates.