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Weighted Non-Crossing Quantile Regressions

Author

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  • Ilaria Lucrezia Amerise

    (Dipartimento di Economia, Statistica e Finanza, Università della Calabria)

Abstract

In this article we are concerned with a collection of multiple linear regressions that enable the researcher to gain an impression of the entire conditional distribution of a response variable given the speci cations for the explanatory variables. In particular, we investigate the advantage of using a new method of parametric estimation for non-crossing quantile regressions. The main tool is a weighting system of the observations that aims to reduce the e ect of contamination of the sampled population on the estimated parameters by diminishing the e ect of outliers. The performance of the new estimators has been evaluated on a number of data sets. We had considerable success with avoiding intersections and in the same time improving the global tting of conditional quantile regressions. We conjecture that in other situations (e.g. data with high level of skewness, non-constant variances, unusual and uncertain data) the method of weighted non-crossing quantiles will lead to estimators with good robustness properties.

Suggested Citation

  • Ilaria Lucrezia Amerise, 2013. "Weighted Non-Crossing Quantile Regressions," Working Papers 201308, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    • Handle: RePEc:clb:wpaper:201308
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    File URL: http://www.ecostat.unical.it/RePEc/WorkingPapers/WP08_2013.pdf
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    References listed on IDEAS

    as
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    6. P. J. Heagerty & M. S. Pepe, 1999. "Semiparametric estimation of regression quantiles with application to standardizing weight for height and age in US children," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(4), pages 533-551.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    conditional quantiles; monotonicity problem; estimation under constraints;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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