Identification-Robust Nonparametric Inference in a Linear IV Model

Bertille Antoine and Pascal Lavergne

No 19-1004, TSE Working Papers from Toulouse School of Economics (TSE)

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 the conditionality principle used by Moreira (2003) 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 applications.

Keywords: Weak Instruments; Hypothesis Testing; Semiparametric Model (search for similar items in EconPapers)
Date: 2019-04-15, Revised 2021-05
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Identification-robust nonparametric inference in a linear IV model (2023)
Working Paper: Identification-Robust Nonparametric Inference in a Linear IV Model (2023)
Working Paper: Identifcation-Robust Nonparametric Inference in a Linear IV Model (2021)
Working Paper: Identification-Robust Nonparametric Inference in a Linear IV Model (2019)
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