On Describing Multivariate Skewness: A Directional Approach

Jose Tome Ferreira () and Mark Steel ()

Econometrics from EconWPA

Abstract: Most multivariate measures of skewness in the literature measure the overall skewness of a distribution. While these measures are perfectly adequate for testing the hypothesis of distributional symmetry, their relevance for describing skewed distributions is less obvious. In this article, we consider the problem of characterising the skewness of multivariate distributions. We define directional skewness as the skewness along a direction and analyse parametric classes of skewed distributions using measures based on directional skewness. The analysis brings further insight into the classes, allowing for a more informed selection of particular classes for particular applications. In the context of Bayesian linear regression under skewed error we use the concept of directional skewness twice. First in the elicitation of a prior on the parameters of the error distribution, and then in the analysis of the skewness of the posterior distribution of the regression residuals.

Keywords: Bayesian methods; Multivariate distribution; Multivariate regression; Prior elicitation; Skewness. (search for similar items in EconPapers)
JEL-codes: C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2004-09-20
Note: Type of Document - pdf; pages: 21
View list of references View citations in EconPapers

Downloads: (external link)
http://129.3.20.41/eps/em/papers/0409/0409010.pdf (application/pdf)

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: http://EconPapers.repec.org/RePEc:wpa:wuwpem:0409010

Access Statistics for this paper

More papers in Econometrics from EconWPA
Series data maintained by EconWPA ().