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Econometrics 2015, 3(3), 654-666;

On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study

Department of Economics, University of Iowa, W284 PBB, 21 E. Market Street, Iowa City, IA 52242, USA
CONICET-Universidad de San Andrés, Vito Dumas 284, Victoria, B1644BID, Pcia. de Bs. As., Argentina
Department of Economics, City University London, Northampton Square, London EC1V 0HB, UK
Author to whom correspondence should be addressed.
Academic Editor: Kerry Patterson
Received: 26 June 2015 / Revised: 10 August 2015 / Accepted: 19 August 2015 / Published: 10 September 2015
(This article belongs to the Special Issue Quantile Methods)
Full-Text   |   PDF [229 KB, uploaded 10 September 2015]


This paper evaluates bootstrap inference methods for quantile regression panel data models. We propose to construct confidence intervals for the parameters of interest using percentile bootstrap with pairwise resampling. We study three different bootstrapping procedures. First, the bootstrap samples are constructed by resampling only from cross-sectional units with replacement. Second, the temporal resampling is performed from the time series. Finally, a more general resampling scheme, which considers sampling from both the cross-sectional and temporal dimensions, is introduced. The bootstrap algorithms are computationally attractive and easy to use in practice. We evaluate the performance of the bootstrap confidence interval by means of Monte Carlo simulations. The results show that the bootstrap methods have good finite sample performance for both location and location-scale models. View Full-Text
Keywords: quantile regression; bootstrap; fixed effects quantile regression; bootstrap; fixed effects
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Galvao, A.F.; Montes-Rojas, G. On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study. Econometrics 2015, 3, 654-666.

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