Identification-robust Inference for Endogeneity Parameters in Models with an Incomplete Reduced Form

Jean-Marie Dufour and Vinh Nguyen

A chapter in Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology, 2022, vol. 43B, pp 337-355 from Emerald Group Publishing Limited

Abstract: The authors propose inference methods for endogeneity parameters in linear simultaneous equation models allowing for weak identification and missing instruments. Endogeneity parameters measure the impact of unobserved variables which may be correlated with observed explanatory variables, and play a central role in determining the “bias” associated with endogeneity and measurement errors in structural equations. These results expand, in several ways, the finite-sample theory inDoko Tchatoka and Dufour (2014)for this problem. The latter theory relies on relatively restrictive assumptions, in particular the hypothesis that the reduced form is complete (e.g., contains all the relevant instruments), which is questionable in many practical situations. While the new proposed inference methods retain identification robustness, they also allow the reduced form to be incomplete, for example, due to missing instruments. The authors propose easily applicable inference methods for endogeneity parameters – in particular, two-stage procedures (similar to those inDufour, 1990). An application to a model of returns to schooling is presented.

Keywords: Endogeneity; exogeneity; instrumental variables; simultaneous equations; IV regression; missing instruments; identification; identification robust; projection; hypothesis testing; confidence set; C01; C12; C26; C3; C36; C52; D1; E2; 62; 62P20; 62P25; 62F03; 62F05; 62F25 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-90532021000043b014

DOI: 10.1108/S0731-90532021000043B014

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