Identification-robust nonparametric inference in a linear IV model

Bertille Antoine and Pascal Lavergne

Journal of Econometrics, 2023, vol. 235, issue 1, 1-24

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.

Keywords: Weak instruments; Hypothesis testing; Semiparametric model (search for similar items in EconPapers)
JEL-codes: C12 C13 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030440762200046X
Full text for ScienceDirect subscribers only

Related works:
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 (2021)
Working Paper: Identification-Robust Nonparametric Inference in a Linear IV Model (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:235:y:2023:i:1:p:1-24

DOI: 10.1016/j.jeconom.2022.01.011

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().