A Monte Carlo comparison of parametric and nonparametric quantile regressions
This study compares parametric and nonparametric quantile regression methods using Monte Carlo simulations. Simulation results indicate that the nonparametric quantile regression approach is more appropriate, particularly when the underlying model is nonlinear or the error term follows a non-normal distribution.
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Volume (Year): 11 (2004)
Issue (Month): 2 ()
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- Jose A. F. Machado & Jose Mata, 2000. "Box-Cox quantile regression and the distribution of firm sizes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(3), pages 253-274.
- Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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