Finite sample inference for quantile regression models
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Chernozhukov_Finite sample.pdf
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1.6 MB
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Author(s)
Hansen, Christian
Jansson, Michael
Chernozhukov, Victor V.
Date Issued
January 2009
Journal
Journal of Econometrics
Publisher
Elsevier
Citation
Chernozhukov, Victor, Christian Hansen, and Michael Jansson. “Finite Sample Inference for Quantile Regression Models.” Journal of Econometrics 152, no. 2 (October 2009): 93–103.
Version
Author's final manuscript
Abstract
Under minimal assumptions, finite sample confidence bands for quantile regression models can be constructed. These confidence bands are based on the “conditional pivotal property” of estimating equations that quantile regression methods solve and provide valid finite sample inference for linear and nonlinear quantile models with endogenous or exogenous covariates. The confidence regions can be computed using Markov Chain Monte Carlo (MCMC) methods. We illustrate the finite sample procedure through two empirical examples: estimating a heterogeneous demand elasticity and estimating heterogeneous returns to schooling. We find pronounced differences between asymptotic and finite sample confidence regions in cases where the usual asymptotics are suspect.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
Creative Commons Attribution-Noncommercial-NoDerivatives
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DOI of Published Version
https://doi.org/10.1016/j.jeconom.2009.01.004
