Doko Tchatoka, Firmin (2010): Subset hypotheses testing and instrument exclusion in the linear IV regression.

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Abstract
This paper explores the sensitivity of plugin based subset tests to instrument exclusion in linear IV regression. Recently, identificationrobust statistics based on plugin principle have been developed for testing hypotheses specified on subsets of the structural parameters. However, their robustness to instrument exclusion has not been investigated. Instrument exclusion is an important problem in econometrics and there are at least two reasons to be concerned. Firstly, it is difficult in practice to assess whether an instrument has been omitted. Secondly, in many instrumental variable (IV) applications, an infinite number of instruments are available for use in large sample estimation. This is particularly the case with most time series models. If a given variable, say X(t), is a legitimate instrument, so too are its lags X(t1), X(t2), ... Hence, instrument exclusion seems highly likely in most practical situations. In this paper, we stress that the usual high level assumption of the identification may be misleading when potential relevant instruments are omitted. We propose an analysis of the asymptotic distributions of the LIML estimator and the plugin based statistics when potential instrument are omitted. Our results provides several new insights and extensions of earlier studies. We show that even when partial identification holds, the asymptotic distribution of the LIML estimator of the identified linear combination is no longer a Gaussian mixture, even though it is still consistent. This contrasts with the usual IV estimator of the identified linear combination, which is still asymptotically a Gaussian mixture despite the exclusion of relevant instruments. As a result, the asymptotic distributions of the plugin based subset statistics that exploit the LIML estimator are modified in a way that could lead to size distortions. We provide an empirical illustration using a widely considered returns to education example, which clearly shows that the confidence sets of the returns to education resulting from the plugin principle are highly sensitive to instrument exclusion.
Item Type:  MPRA Paper 

Original Title:  Subset hypotheses testing and instrument exclusion in the linear IV regression 
Language:  English 
Keywords:  Instrument exclusion, robust subset tests, LIML estimator, consistency, size distortions 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C12  Hypothesis Testing: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C30  General 
Item ID:  29611 
Depositing User:  Firmin Doko Tchatoka 
Date Deposited:  16 Aug 2012 12:27 
Last Modified:  02 Oct 2019 20:57 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/29611 