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Quantile uncorrelation and instrumental regressions

Komarova, Tatiana ORCID: 0000-0002-6581-5097, Severini, Thomas A. and Tamer, Elie T. (2012) Quantile uncorrelation and instrumental regressions. Journal of Econometric Methods, 1 (1). pp. 2-14. ISSN 2156-6674

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Identification Number: 10.1515/2156-6674.1001


We introduce a notion of median uncorrelation that is a natural extension of mean (linear) uncorrelation. A scalar random variable Y is median uncorrelated with a k-dimensional random vector X if and only if the slope from an LAD regression of Y on X is zero. Using this simple definition, we characterize properties of median uncorrelated random variables, and introduce a notion of multivariate median uncorrelation. We provide measures of median uncorrelation that are similar to the linear correlation coefficient and the coefficient of determination. We also extend this median uncorrelation to other loss functions. As two stage least squares exploits mean uncorrelation between an instrument vector and the error to derive consistent estimators for parameters in linear regressions with endogenous regressors, the main result of this paper shows how a median uncorrelation assumption between an instrument vector and the error can similarly be used to derive consistent estimators in these linear models with endogenous regressors. We also show how median uncorrelation can be used in linear panel models with quantile restrictions and in linear models with measurement errors.

Item Type: Article
Official URL:
Additional Information: © 2012 de Gruyter
Divisions: Economics
Subjects: H Social Sciences > HB Economic Theory
JEL classification: C - Mathematical and Quantitative Methods > C0 - General
Date Deposited: 16 Oct 2012 13:54
Last Modified: 03 Jun 2022 18:03

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