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A simulation algorithm for non-causal VARMA processes

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  • Giurcanu, Mihai C.

Abstract

We propose a simulation algorithm for non-causal vector autoregressive moving average (VARMA) processes. The algorithm is based on the Jordan canonical form of the companion matrix in the state space representation. We illustrate its performance for a non-causal V ARMA(2,2) process.

Suggested Citation

  • Giurcanu, Mihai C., 2015. "A simulation algorithm for non-causal VARMA processes," Statistics & Probability Letters, Elsevier, vol. 98(C), pages 65-72.
    • Handle: RePEc:eee:stapro:v:98:y:2015:i:c:p:65-72
    DOI: 10.1016/j.spl.2014.12.005
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    References listed on IDEAS

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    1. Lanne, Markku & Saikkonen, Pentti, 2013. "Noncausal Vector Autoregression," Econometric Theory, Cambridge University Press, vol. 29(3), pages 447-481, June.
    2. Esam Mahdi & A. Ian McLeod, 2012. "Improved multivariate portmanteau test," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(2), pages 211-222, March.
    3. Henri Nyberg & Markku Lanne & Erkka Saarinen, 2012. "Does noncausality help in forecasting economic time series?," Economics Bulletin, AccessEcon, vol. 32(4), pages 2849-2859.
    4. Breid, F. Jay & Davis, Richard A. & Lh, Keh-Shin & Rosenblatt, Murray, 1991. "Maximum likelihood estimation for noncausal autoregressive processes," Journal of Multivariate Analysis, Elsevier, vol. 36(2), pages 175-198, February.
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