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A Monte Carlo comparison of parametric and nonparametric quantile regressions

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  • Insik Min
  • Inchul Kim

Abstract

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

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    1. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
    2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    3. 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.
    4. Newey, Whitney K & Powell, James L, 1987. "Asymmetric Least Squares Estimation and Testing," Econometrica, Econometric Society, vol. 55(4), pages 819-847, July.
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    Cited by:

    1. Corrado Andini, 2010. "Within-groups wage inequality and schooling: further evidence for Portugal," Applied Economics, Taylor & Francis Journals, vol. 42(28), pages 3685-3691.
    2. Insik Min, 2007. "A nonparametric test of the conditional normality of housing demand," Applied Economics Letters, Taylor & Francis Journals, vol. 14(2), pages 105-109.
    3. Anil Kumar, 2006. "Nonparametric conditional density estimation of labour force participation," Applied Economics Letters, Taylor & Francis Journals, vol. 13(13), pages 835-841.
    4. Manuel Landajo & Javier De Andrés & Pedro Lorca, 2008. "Measuring firm performance by using linear and non‐parametric quantile regressions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(2), pages 227-250, April.
    5. Meng-Shiuh Chang & Teng-Yuan Hu & Ching-Yuan Lin, 2016. "Variation in Engel's law across quantiles in Taiwan: toward an alternative concept of near poverty line," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 21(1), pages 103-115, January.

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