Abstract:
We study the causal interpretation of instrumental variables (IV) estimands of nonlinear, multivariate structural models with respect to rich forms of model misspecification. We focus on guaranteeing that the researcher’s estimator is sharp zero consistent, meaning that the researcher concludes that the endogenous variable has no causal effect on the outcome whenever this is actually the case. Sharp zero consistency generally requires the researcher’s estimator to satisfy a condition that we call strong exclusion. When a researcher has access to excluded, exogenous variables, strong exclusion can often be achieved by appropriate choice of estimator and instruments. Failure of strong exclusion can lead to large bias in estimates of causal effects in realistic situations. Our results cover many settings of interest including models of differentiated goods demand with endogenous prices and models of production with endogenous inputs.