Identification and Inference for Econometric Models
Essays in Honor of Thomas Rothenberg
Edited by Donald W. K. Andrews
Edited by James H. Stock
Publisher: Cambridge University Press
Print Publication Year: 2005
Online Publication Date:February 2010
Chapter DOI: http://dx.doi.org/10.1017/CBO9780511614491.006
Weak instruments can produce biased IV estimators and hypothesis tests with large size distortions. But what, precisely, are weak instruments, and how does one detect them in practice? This paper proposes quantitative definitions of weak instruments based on the maximum IV estimator bias, or the maximum Wald test size distortion, when there are multiple endogenous regressors. We tabulate critical values that enable using the first-stage F-statistic (or, when there are multiple endogenous regressors, the Cragg–Donald  statistic) to test whether the given instruments are weak.
Standard treatments of instrumental variables (IV) regression stress that for instruments to be valid they must be exogenous. It is also important, however, that the second condition for a valid instrument, instrument relevance, holds, for if the instruments are only marginally relevant, or “weak,” then first-order asymptotics can be a poor guide to the actual sampling distributions of conventional IV regression statistics.
At a formal level, the strength of the instruments matters because the natural measure of this strength – the so-called concentration parameter – plays a role formally akin to the sample size in IV regression statistics. Rothenberg (1984) makes this point in his survey of approximations to the distributions of estimators and test statistics.