1995
DOI: 10.1016/0304-4076(94)01652-g
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Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study

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Cited by 371 publications
(237 citation statements)
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“…We solved the problem stated in Eq. (4) using the linear programming algorithm proposed by Koenker and D'Orey (1987) and computed the standard error for the estimated parameters using the bootstrapping procedure proposed by Buchinsky (1995). As for the DiD, as outlined in Section 2.2 above, we estimated Eq.…”
Section: Difference-in-differences Framework Via Quantile Regressionmentioning
confidence: 99%
“…We solved the problem stated in Eq. (4) using the linear programming algorithm proposed by Koenker and D'Orey (1987) and computed the standard error for the estimated parameters using the bootstrapping procedure proposed by Buchinsky (1995). As for the DiD, as outlined in Section 2.2 above, we estimated Eq.…”
Section: Difference-in-differences Framework Via Quantile Regressionmentioning
confidence: 99%
“…where Z is an n x k matrix of explanatory variables, fJo, is a k x 1 vector of coefficients to be izing bootstrap methods (for details see Buchinsky, 1994Buchinsky, , 1995.…”
Section: Estimation Methodsologymentioning
confidence: 99%
“…Other estimators may be derived for specific cases, e.g., for the IV QR example, Powell's (1984) consistent estimator for G isĜ P = P n i=1 1(¯¯y i − w 0 iβ θ¯<ĉn )x i w 0 i /2ĉ n n whereĉ n p → 0 and satisfies the other regularity conditions stated in Powell (1984); see section 5.1. The Monte-Carlo study of Buchinsky (1995) for QR showed that the performance of kernel-based estimators, e.g.,Ĝ P , depends critically on the choice of kernel and bandwidth, the latter problem also shared by the numerical derivative estimator.…”
Section: Asymptotic Variance Matrix Estimationmentioning
confidence: 99%