RANDOM AGGREGATION OF UNIVARIATE AND MULTIVARIATE LINEAR PROCESSES

A. Kadi, G. Oppenheim and M. C. Viano

Journal of Time Series Analysis, 1994, vol. 15, issue 1, 31-43

Abstract: Abstract. The paper is devoted to random aggregation of multivariate autoregressive moving‐average (ARMA) processes. We derive second‐order characteristics of random aggregate models. We show that random aggregation preserves the ARMA structure. Moreover, we specify a functional relation between the initial model poles and aggregate ones. We then examine the case of univariate ARMA processes. Theorem 4 shows that, if the initial process is ARMA(p, q), the random aggregate process is an ARMA(p*, q*) model with p* at most equal to p; * depends, among other things, on the sampling distribution L. This theorem generalizes the well‐known results on the topic of time interval aggregation without overlapping.

Date: 1994
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https://doi.org/10.1111/j.1467-9892.1994.tb00175.x

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Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:15:y:1994:i:1:p:31-43

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