On bootstrap inference for quantile regression panel data: A Monte Carlo study

Artikel

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 3 ; Year: 2015 ; Issue: 3 ; Pages: 654-666 ; Basel: MDPI

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Estimation: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
• Thema: quantile regression ; bootstrap ; fixed effects

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Galvão Júnior, Antônio Fialho ; Montes-Rojas, Gabriel
• Ereignis: Veröffentlichung
• (wer): MDPI
• (wo): Basel
• (wann): 2015

Weitere Informationen

• DOI: doi:10.3390/econometrics3030654
• Handle: http://hdl.handle.net/10419/171844
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ