Implementing Box-Cox Quantile Regression
The Box-Cox quantile regression model introduced by Powell (1991) is a flexible and numerically attractive extension of linear quantile regression techniques. Chamberlain (1994) and Buchinsky (1995) suggest a two stage estimator for this model but the objective function in stage two of their method may not be defined in an application. We suggest a modification of the estimator which is easy to implement. A simulation study demonstrates that the modified estimator works well in situations, where the original estimator is not well defined.
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Volume (Year): 29 (2010)
Issue (Month): 2 (April)
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References listed on IDEAS
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- Fitzenberger, Bernd, 1998. "The moving blocks bootstrap and robust inference for linear least squares and quantile regressions," Journal of Econometrics, Elsevier, vol. 82(2), pages 235-287, February.
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- Xuan, Zhang & Wilke, Ralf A. & Lüdemann, Elke, 2005. "Censored Quantile Regressions and the Length of Unemployment Periods in West Germany," ZEW Discussion Papers 04-57 [rev.], ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
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