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Efficient semiparametric seemingly unrelated quantile regression estimation. (English) Zbl 1284.62419
Summary: We propose an efficient semiparametric estimator for the coefficients of a multivariate linear regression model – with a conditional quantile restriction for each equation – in which the conditional distributions of errors given regressors are unknown. The procedure can be used to estimate multiple conditional quantiles of the same regression relationship. The proposed estimator is asymptotically as efficient as if the true optimal instruments were known. Simulation results suggest that the estimation procedure works well in practice and dominates an equation-by-equation efficiency correction if the errors are dependent conditional on the regressors.

MSC:
62J05 Linear regression; mixed models
62F12 Asymptotic properties of parametric estimators
62G05 Nonparametric estimation
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