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On a characterization of optimal predictors for nonstationary ARMA processes

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  • Kowalski, Aleksander
  • Szynal, Dominik

Abstract

This note contains a characterization of predictors for nonstationary ARMA processes. Moreover, we give the weak law of large numbers for those processes.

Suggested Citation

  • Kowalski, Aleksander & Szynal, Dominik, 1991. "On a characterization of optimal predictors for nonstationary ARMA processes," Stochastic Processes and their Applications, Elsevier, vol. 37(1), pages 71-80, February.
    • Handle: RePEc:eee:spapps:v:37:y:1991:i:1:p:71-80
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    Cited by:

    1. 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.
    2. 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.
    3. 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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