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Identification of Nonzero Elements in the Polynomial Matrices of MixedVarmaProcesses

S. G. Koreisha, T. M. Pukkila

Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 1987 · Volume 49 · Issue 1

DOI: 10.1111/j.2517-6161.1987.tb01432.x

Abstract

The parameter density is the polynomial matrices of vector autoregressive moving average (VARMA) time series models is generally very low. Thus, estimation of fully parameterized high order models or of models with many variables is an ineffective, if not impractical, way of utilizing computing power. In the article we present methods for identifying the nonzero elements inVARMA(1,1) models, and show how these procedures can be used to construct models of higher order.

Keywords

multiple time series, polynomial matrix density, sum of squares function, long and successive autoregressions

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