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Efficient generation of covariance sequences of multiple ARMA processes

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1 Author(s)
S. Mittnik ; Dept. of Econ., State Univ. of New York, Stony Brook, NY, USA

An efficient procedure for computing autocovariance sequences of multiple autoregressive moving-average processes is proposed. While the computational complexity of existing algorithms is proportional to p 3, where p denotes the degree of the autoregressive polynomial, the procedure proposed has a complexity of O( p2). The resulting scheme leads to substantial computational savings, especially when dealing with processes with autoregressive polynomials of high degree and therefore facilitates the estimation of multiple autoregressive moving-average processes with exact maximum-likelihood methods

Published in:

Decision and Control, 1988., Proceedings of the 27th IEEE Conference on

Date of Conference:

7-9 Dec 1988