Identification-Robust Nonparametric Inference in a Linear IV Model

Abstract

For a linear IV regression, we propose two new inference procedures on parameters of endogenous variables that are robust to any identification pattern, do not rely on a linear first-stage equation, and account for heteroskedasticity of unknown form. Building on Bierens (1982), we first propose an Integrated Conditional Moment (ICM) type statistic constructed by setting the parameters to the value under the null hypothesis. The ICM procedure tests at the same time the value of the coefficient and the specification of the model. We then adopt a conditionality principle to condition on a set of ICM statistics that informs on identification strength. Our two procedures uniformly control size irrespective of identification strength. They are powerful irrespective of the nonlinear form of the link between instruments and endogenous variables and are competitive with existing procedures in simulations and application.

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Replaces

Bertille Antoine, and Pascal Lavergne, “Identification-Robust Nonparametric Inference in a Linear IV Model”, TSE Working Paper, n. 19-1004, April 2019, revised May 2021.

Reference

Bertille Antoine, and Pascal Lavergne, “Identification-Robust Nonparametric Inference in a Linear IV Model”, Journal of Econometrics, vol. 234, n. 1, July 2023, pp. 1–24.