Adaptive semiparametric M-quantile regression

Fabian Otto-Sobotka, Nicola Salvati, Maria Giovanna Ranalli and Thomas Kneib

Econometrics and Statistics, 2019, vol. 11, issue C, 116-129

Abstract: Parametric and semiparametric regression beyond the mean have become important tools for multivariate data analysis in this world of heteroscedasticity. Among several alternatives, quantile regression is a very popular choice if regression on more than a location measure is desired. This is also due to the inherent robustness of a quantile estimate. However, when moving towards the tails of a distribution, the handling of extreme observations becomes crucial for empirical estimates. M-quantiles handle outliers within the regression analysis by imposing a strong robustness to the loss function. However, this loss function is typically not designed to handle heteroscedasticity. An adaptive extension to the degree of robustness within the loss function is proposed along with the implementation of semiparametric predictors in an M-quantile regression model. A practical method to compute confidence intervals is also presented. The methods are supported by extensive simulations and an analysis of childhood malnutrition in Tanzania.

Keywords: Iteratively weighted least squares; P-splines; Semiparametric regression; Heteroscedasticity; Two-stage estimation; Expectiles (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S245230621930019X
Full text for ScienceDirect subscribers only. Contains open access articles

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:11:y:2019:i:c:p:116-129

DOI: 10.1016/j.ecosta.2019.03.001

Access Statistics for this article

Econometrics and Statistics is currently edited by E.J. Kontoghiorghes, H. Van Dijk and A.M. Colubi

More articles in Econometrics and Statistics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().