WEIGHTED NON-CROSSING QUANTILE REGRESSIONS

Ilaria Lucrezia Amerise ()
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Ilaria Lucrezia Amerise: Dipartimento di Economia, Statistica e Finanza, Università della Calabria

No 201308, Working Papers from Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF

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.

Keywords: conditional quantiles; monotonicity problem; estimation under constraints (search for similar items in EconPapers)
JEL-codes: C21 C31 C6 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2013-11
New Economics Papers: this item is included in nep-ecm
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http://www.ecostat.unical.it/RePEc/WorkingPapers/WP08_2013.pdf First version, 2013-11 (application/pdf)

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