Implementing Weak-Instrument Robust Tests for a General Class of Instrumental-Variables Models

Abstract

We present a minimum distance approach for conducting hypothesis testing in the presence of potentially weak instruments. Under this approach, we propose size-correct tests for limited dependent variable models with endogenous explanatory variables such as endogenous tobit and probit models. Additionally, we extend weak-instrument tests for the linear instrumental-variables model by allowing for variance–covariance estimation that is robust to arbitrary heteroskedasticity or intracluster dependence. We invert these tests to construct confidence intervals on the coefficient of the endogenous variable. We also provide a postestimation command for Stata, called rivtest, for computing the tests and estimating confidence intervals.