Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study
通过蒙特卡洛模拟，比较了分位数回归和删失分位数回归模型中六种渐近协方差矩阵估计方法的表现，使用1987年当前人口调查数据作为样本。
This Monte Carlo study examines several estimation procedures of the asymptotic covariance matrix in the quantile and censored quantile regression models: design matrix bootstrap, error bootstrapping, order statistic, sigma bootstrap, homoskedastic kernel, and heteroskedastic kernel. The Monte Carlo samples are drawn from two alternative data sets: (a) the unaltered Current Population Survey (CPS) for 1987 and (b) this CPS data with independence between error term and regressors imposed.