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Autoren: 
Galvão Júnior, Antônio Fialho
Montes-Rojas, Gabriel
Datum: 
2015
Quellenangabe: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 3 [Year:] 2015 [Issue:] 3 [Pages:] 654-666
Zusammenfassung: 
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.
Schlagwörter: 
quantile regression
bootstrap
fixed effects
JEL: 
C13
C21
C23
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
http://creativecommons.org/licenses/by/4.0/
Dokumentart: 
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