Bootstrapping Quantile Regression Estimators

B-Tier
Journal: Econometric Theory
Year: 1995
Volume: 11
Issue: 1
Pages: 105-121

Score contribution per author:

2.011 = (α=2.01 / 1 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

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.

Technical Details

RePEc Handle
repec:cup:etheor:v:11:y:1995:i:01:p:105-121_00
Journal Field
Econometrics
Author Count
1
Added to Database
2026-01-25