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Specification Analysis of Structural Quantile Regression Models

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  • Juan Carlos Escanciano
  • Chuan Goh

Abstract

This paper introduces a broad family of tests for the hypothesis of linearity in parameters of functions that are identified by conditional quantile restrictions involving instrumental variables. These tests are tantamount to assessments of lack of fit for quantile regression models involving endogenous conditioning variables, and may be applied to assess the validity of post-estimation inferences regarding the counterfactual effect of endogenous treatments on the distribution of outcomes. We show that the use of an orthogonal projection on the tangent space of nuisance parameters at each quantile index improves power performance and facilitates the simulation of critical values via the application of simple multiplier-type bootstrap procedures. Monte Carlo evidence is included, along with an application to an empirical analysis of the structure of demand for a particular subsegment of the market for anti-bacterial drugs in India.

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Bibliographic Info

Paper provided by University of Toronto, Department of Economics in its series Working Papers with number tecipa-415.

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Length: 52 pages
Date of creation: 19 Nov 2010
Date of revision:
Handle: RePEc:tor:tecipa:tecipa-415

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Keywords: Quantile regression; instrumental variables; structural models;

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References

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Cited by:
  1. Christoph Rothe & Dominik Wied, 2013. "Misspecification Testing in a Class of Conditional Distributional Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 314-324, March.

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