Computing the best linear predictor in a Hilbert space. Applications to general ARMAH processes

This article deals with linear prediction in large dimensions. One obtains various explicit forms of the best linear predictor in a Hilbert space. The difficulty comes from the fact that the associated linear operator is, in general, not continuous. Applications to ARMAH processes, models with noise and Bayesian estimators are considered.

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Year of publication:	2014
Authors:	Bosq, D.
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 124.2014, C, p. 436-450
Publisher:	Elsevier
Subject:	Functional filters \| Hilbert spaces \| Linear processes \| Measurable linear transformations \| Prediction \| Large dimensions

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010737768