Finite sample inference for quantile regression models

Victor Chernozhukov, Christian Hansen and Michael Jansson ()

Journal of Econometrics, 2009, vol. 152, issue 2, pages 93-103

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.

Keywords: Extremal; quantile; regression; Instrumental; quantile; regression; Partial; identification; Weak; identification (search for similar items in EconPapers)
Date: 2009

Downloads: (external link)
http://www.sciencedirect.com/science/article/B6VC0 ... 4651e48a1d3ffdf64c79
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:eee:econom:v:152:y:2009:i:2:p:93-103

Access Statistics for this article

Journal of Econometrics is edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao, P. M. Robinson and A. Zellner

More articles in Journal of Econometrics from Elsevier
Series data maintained by Heidi Boesdal ().