Innovations algorithm for periodically stationary time series
Periodic ARMA, or PARMA, time series are used to model periodically stationary time series. In this paper we develop the innovations algorithm for periodically stationary processes. We then show how the algorithm can be used to obtain parameter estimates for the PARMA model. These estimates are proven to be weakly consistent for PARMA processes whose underlying noise sequence has either finite or infinite fourth moment. Since many time series from the fields of economics and hydrology exhibit heavy tails, the results regarding the infinite fourth moment case are of particular interest.
Year of publication: |
1999
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Authors: | Anderson, Paul L. ; Meerschaert, Mark M. ; Vecchia, Aldo V. |
Published in: |
Stochastic Processes and their Applications. - Elsevier, ISSN 0304-4149. - Vol. 83.1999, 1, p. 149-169
|
Publisher: |
Elsevier |
Keywords: | Time series Periodically stationary Yule-Walker estimates Innovations algorithm Heavy tails Regular variation |
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