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Bootstrapping Quantile Regression Estimators

The asymptotic variance matrix of the quantile regression estimator depends on the density of the error. For both deterministic and random regressors, the bootstrap distribution is shown to converge weakly to the limit distribution of the quantile regression estimator in probability. Thus, the confidence intervals constructed by the bootstrap percentile method have asymptotically correct coverage probabilities.
Year of publication:
1995
Authors: Hahn, Jinyong
Published in:
Econometric Theory. - Cambridge University Press. - Vol. 11.1995, 01, p. 105-121
Publisher: Cambridge University Press
Description of contents: Abstract [journals.cambridge.org]
Type of publication: Article
Source:
Persistent link: https://www.econbiz.de/10005411777
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