The Principle of Overcompleteness in Multivariate Economic Time Series Models

Result code in IS VaVaI

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Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

The Principle of Overcompleteness in Multivariate Economic Time Series Models

Original language description

In this paper we apply the principle of overcompleteness to sparse parameter estimation in multivariate ARMA models (VARMA models). This new approach is based on the Basis Pursuit Algorithm originally suggested by Chen et al [1]. Overcompleteness means that we admit higher range of orders within which we are looking for lowest possible number of significant parameters (sparsity). A previous study confirmed that this relaxation of the commonly used low-order assumption may yield more precise forecasts from ARMA models when compared with standard statistical estimation techniques. Here an analogical approach will be used for the analysis of multivariate economic time series. It is well-known that particular time series are strongly cross-correlated. Thatis why we expect our technique to be possibly successful for the multivariate case too.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

AH - Economics

OECD FORD branch

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Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2006

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Mathematical Methods in Economics 2006

ISBN

80-7043-479-1

ISSN

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e-ISSN

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Number of pages

6

Pages from-to

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Publisher name

University of Pilsen

Place of publication

Plzeň

Event location

Plzeň

Event date

Jan 1, 2006

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000262064700057