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A Monte Carlo comparison of parametric and nonparametric quantile regressions


  • Insik Min
  • Inchul Kim


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.

Suggested Citation

  • Insik Min & Inchul Kim, 2004. "A Monte Carlo comparison of parametric and nonparametric quantile regressions," Applied Economics Letters, Taylor & Francis Journals, vol. 11(2), pages 71-74.
  • Handle: RePEc:taf:apeclt:v:11:y:2004:i:2:p:71-74 DOI: 10.1080/1350485042000200132

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    References listed on IDEAS

    1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    2. 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.
    3. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
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