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Subset Hypotheses Testing and Instrument Exclusion in the Linear Iv Regression


Doko Tchatoka, S, Subset Hypotheses Testing and Instrument Exclusion in the Linear Iv Regression, Econometric Theory, 31, (6) pp. 1192-1228. ISSN 0266-4666 (2015) [Refereed Article]

Copyright Statement

Copyright 2014 Cambridge University Press

DOI: doi:10.1017/S0266466614000462


his paper explores the sensitivity of plug-in subset tests to instrument exclusion in structural models. Identification-robust statistics based on the plug-in principle have been developed for testing hypotheses specified on subsets of the structural parameters. However, their robustness to instrument exclusion has not been investigated. This paper proposes an analysis of the asymptotic distributions of the limited information maximum likelihood (LIML) estimator and plug-in statistics when potential instruments are omitted. Our results provide several new insights and extensions of earlier studies. We show that the exclusion of instruments can eliminate the first-stage, thus weakening identification and invalidating the plug-in subset inference. However, when instrument omission does not affect LIML consistency, it preserves the plug-in subset test validity, although LIML is no longer asymptotically efficient. Unlike the instrumental variable (IV) estimator, the LIML estimator of the identified linear combination of the nuisance parameter is not asymptotically a Gaussian mixture, even without instrument exclusion.

Item Details

Item Type:Refereed Article
Keywords:forward-look models, asset pricing-models
Research Division:Commerce, Management, Tourism and Services
Research Group:Business systems in context
Research Field:Business systems in context not elsewhere classified
Objective Division:Economic Framework
Objective Group:Other economic framework
Objective Field:Other economic framework not elsewhere classified
UTAS Author:Doko Tchatoka, S (Dr Firmin Doko Tchatoka)
ID Code:119461
Year Published:2015
Web of Science® Times Cited:2
Deposited By:TSBE
Deposited On:2017-08-01
Last Modified:2017-09-27

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