ESTIMATING THE ASYMPTOTIC COVARIANCE-MATRIX FOR QUANTILE REGRESSION-MODELS - A MONTE-CARLO STUDY

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. This special setup allows one to evaluate the estimators under various realistic scenarios. The results favor the design bootstrap for the general case, but also support the order statistic when the error term is independent of the regressors.