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Parameter Estimation for p‐Order Random Coefficient Autoregressive (RCA) Models Based on Kalman Filter

Author

Listed:
  • Mohammed Benmoumen
  • Jelloul Allal
  • Imane Salhi

Abstract

In this paper we elaborate an algorithm to estimate p‐order Random Coefficient Autoregressive Model (RCA(p)) parameters. This algorithm combines quasi‐maximum likelihood method, the Kalman filter, and the simulated annealing method. In the aim to generalize the results found for RCA(1), we have integrated a subalgorithm which calculate the theoretical autocorrelation. Simulation results demonstrate that the algorithm is viable and promising.

Suggested Citation

  • Mohammed Benmoumen & Jelloul Allal & Imane Salhi, 2019. "Parameter Estimation for p‐Order Random Coefficient Autoregressive (RCA) Models Based on Kalman Filter," Journal of Applied Mathematics, John Wiley & Sons, vol. 2019(1).
    • Handle: RePEc:wly:jnljam:v:2019:y:2019:i:1:n:8479086
    DOI: 10.1155/2019/8479086
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    References listed on IDEAS

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    1. A. Thavaneswaran & B. Abraham, 1988. "Estimation For Non‐Linear Time Series Models Using Estimating Equations," Journal of Time Series Analysis, Wiley Blackwell, vol. 9(1), pages 99-108, January.
    2. Liang, Y. & Thavaneswaran, A. & Ravishanker, N., 2013. "RCA models: Joint prediction of mean and volatility," Statistics & Probability Letters, Elsevier, vol. 83(2), pages 527-533.
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