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A note on the properties of some nonstationary ARMA processes

Author

Listed:
  • Singh, N.
  • Peiris, M. Shelton

Abstract

The aim of this note is to study the properties of some nonstationary autoregressive-moving average (ARMA) processes that are considered important in real world situations. In particular, the covariance structure and linear predictors are obtained.

Suggested Citation

  • Singh, N. & Peiris, M. Shelton, 1987. "A note on the properties of some nonstationary ARMA processes," Stochastic Processes and their Applications, Elsevier, vol. 24(1), pages 151-155, February.
    • Handle: RePEc:eee:spapps:v:24:y:1987:i:1:p:151-155
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    Citations

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    Cited by:

    1. Abdelkamel Alj & Christophe Ley & Guy Melard, 2015. "Asymptotic Properties of QML Estimators for VARMA Models with Time-Dependent Coefficients: Part I," Working Papers ECARES ECARES 2015-21, ULB -- Universite Libre de Bruxelles.
    2. Karanasos, Menelaos & Paraskevopoulos,Alexandros & Canepa, Alessandra, 2020. "Unified Theory for the Large Family of Time Varying Models with Arma Representations: One Solution Fits All," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202008, University of Turin.
    3. M. Shelton Peiris & Manabu Asai, 2016. "Generalized Fractional Processes with Long Memory and Time Dependent Volatility Revisited," Econometrics, MDPI, vol. 4(3), pages 1-21, September.
    4. Abdelouahab Bibi & Christian Francq, 2003. "Consistent and asymptotically normal estimators for cyclically time-dependent linear models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(1), pages 41-68, March.
    5. Menelaos Karanasos & Alexandros Paraskevopoulos & Faek Menla Ali & Michail Karoglou & Stavroula Yfanti, 2014. "Modelling Returns and Volatilities During Financial Crises: a Time Varying Coefficient Approach," Papers 1403.7179, arXiv.org.

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