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 variables 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 O (n1/2), where n is the sample size. Monte Carlo simulations illustrate the finite-sample performance of the test.
Authors
![Sokbae "Simon" Lee](/sites/default/files/styles/square_desktop/public/2022-07/Simon%20Lee.jpg?itok=J8PnQSbc)
Research Fellow Columbia University
Sokbae is an IFS Research Fellow and a Professor at Columbia University, with an interest in Econometrics, Applied Microeconomics and Statistics.
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Northwestern University
Journal article details
- Publisher
- Elsevier
- Issue
- Volume 152, Issue 2, October 2009, pages 141-152
Suggested citation
Horowitz, J and Lee, S. (2009). 'Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative' 152(2/2009), pp.141–152.
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