ADAPTIVE ESTIMATION OF ERROR CORRECTION MODELS

Douglas J. Hodgson

Econometric Theory, 1998, vol. 14, issue 01, pages 44-69

Abstract: This paper considers adaptive maximum likelihood estimation of reduced rank vector error correction models. It is shown that such models can be asymptotically efficiently estimated even in the absence of knowledge of the shape of the density function of the innovation sequence, provided that this density is symmetric. The construction of the estimator, involving the nonparametric kernel estimation of the unknown density using the residuals of a consistent preliminary estimator, is described, and its asymptotic distribution is derived. Asymptotic efficiency gains over the Gaussian pseudo maximum likelihood estimator are evaluated for elliptically symmetric innovations.

Date: 1998
View citations in EconPapers

Downloads: (external link)
http://journals.cambridge.org/abstract_S0266466698141026 link to article abstract page (text/html)

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:cup:etheor:v:14:y:1998:i:01:p:44-69_14

Access Statistics for this article

More articles in Econometric Theory from Cambridge University Press
Address: The Edinburgh Building, Shaftesbury Road, Cambridge CB2 2RU UK
Series data maintained by Mike Eden ().