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Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative

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  • Horowitz, Joel L.
  • Lee, Sokbae

Abstract

This paper is concerned with inference about a function g that is identified by a conditional quantile restriction involving instrumental variables. The paper presents a test of the hypothesis that g belongs to a finite-dimensional parametric family against a nonparametric alternative. The test is not subject to the ill-posed inverse problem of nonparametric instrumental variable estimation. Under mild conditions, the test is consistent against any alternative model. In large samples, its power is arbitrarily close to 1 uniformly over a class of alternatives whose distance from the null hypothesis is proportional to n-1/2, where n is the sample size. Monte Carlo simulations illustrate the finite-sample performance of the test.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 152 (2009)
Issue (Month): 2 (October)
Pages: 141-152

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Handle: RePEc:eee:econom:v:152:y:2009:i:2:p:141-152

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Web page: http://www.elsevier.com/locate/jeconom

Related research

Keywords: Hypothesis test Quantile estimation Instrumental variables Specification testing Consistent testing;

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References

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Citations

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Cited by:
  1. Juan Carlos Escanciano & Chuan Goh, 2010. "Specification Analysis of Structural Quantile Regression Models," Working Papers tecipa-415, University of Toronto, Department of Economics.
  2. Horowitz, Joel L., 2012. "Specification testing in nonparametric instrumental variable estimation," Journal of Econometrics, Elsevier, vol. 167(2), pages 383-396.
  3. Escanciano, J.C. & Goh, S.C., 2014. "Specification analysis of linear quantile models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 495-507.

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