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

• Autores: Galvao, Antonio; Montes Rojas, Gabriel Victorio
• Año de publicación: 2015
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: QUANTILE REGRESSION; PANEL DATA; FIXED EFFECTS
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

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.
Fil: Galvao, Antonio. University of Iowa; Estados Unidos
Fil: Montes Rojas, Gabriel Victorio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. City University of London; Reino Unido