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Characterization of periodically correlated and multivariate stationary discrete time wide Markov processes

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  • Castro, Glaysar
  • Girardin, Valerie

Abstract

The aim of this paper is to give an overview of the structure of the class of discrete time wide Markov processes, either periodically correlated or multivariate stationary. We show many properties of their covariance, correlation and reflection coefficients matrices. We characterize these processes chiefly in terms of autoregressive models of order one. Illustrative numerical examples are given.

Suggested Citation

  • Castro, Glaysar & Girardin, Valerie, 2008. "Characterization of periodically correlated and multivariate stationary discrete time wide Markov processes," Statistics & Probability Letters, Elsevier, vol. 78(2), pages 158-164, February.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:2:p:158-164
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    References listed on IDEAS

    as
    1. Castro, Glaysar & Girardin, Valerie, 2002. "Maximum of entropy and extension of covariance matrices for periodically correlated and multivariate processes," Statistics & Probability Letters, Elsevier, vol. 59(1), pages 37-52, August.
    2. Lai, C. D., 1978. "First order autoregressive markov processes," Stochastic Processes and their Applications, Elsevier, vol. 7(1), pages 65-72, March.
    3. Franses, Philip Hans, 1996. "Periodicity and Stochastic Trends in Economic Time Series," OUP Catalogue, Oxford University Press, number 9780198774549.
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