Some Geometry for the Maximal Invariant in Linear Regression

Patrick Marsh ()

Discussion Papers from Department of Economics, University of York

Abstract: The maximal invariant forms the basis of a well established theory on hypothesis testing on the covariance structure in linear regression, see Lehman (1997). This paper examines the geometry of the maximal invariant. In particular it derives explicit expressions for both Fisher information and statistical curvature, see Efron (1975). The results apply for any sample size, for any sufficiently differentiable covariance structure and across a variety of sample densities. The results are illustrated for regressions involving autoregressive and moving average errors. Specifically, the effects of different specifications of the mean and of non-stationarity and non-invertibility may be quantified.

Keywords: Differential geometry; Efron curvature; Fisher information; maximal invariant (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.york.ac.uk/media/economics/documents/discussionpapers/2004/0407.pdf Main text (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: https://EconPapers.repec.org/RePEc:yor:yorken:04/07

Access Statistics for this paper

More papers in Discussion Papers from Department of Economics, University of York Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom. Contact information at EDIRC.
Bibliographic data for series maintained by Paul Hodgson ().