Title: Robust Methods for Heteroskedastic Regression
Authors: ATKINSON Anthony C.RIANI MarcoTORTI Francesca
Citation: COMPUTATIONAL STATISTICS & DATA ANALYSIS vol. 104 p. 209-222
Publisher: ELSEVIER SCIENCE BV
Publication Year: 2016
JRC N°: JRC91063
ISSN: 0167-9473
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC91063
DOI: 10.1016/j.csda.2016.07.002
Type: Articles in periodicals and books
Abstract: Heteroskedastic regression data are modelled using a parameterized variance function. This procedure is robustified using a method with high breakdown point and high efficiency, which provides a direct link between observations and the weights used in model fitting. This feature is vital for the application, the analysis of international trade data from the European Union. Heteroskedasticity is strongly present in such data, as are outliers. A further example shows that the new method outperforms ordinary least squares with heteroskedasticity robust standard errors, even when the form of heteroskedasticity is mis-specified. A discussion of computational matters concludes the paper. An appendix presents the new scoring algorithm for estimation of the parameters of heteroskedasticity.
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