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

Author

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  • 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, Hindawi, vol. 2019, pages 1-5, May.
    • Handle: RePEc:hin:jnljam:8479086
    DOI: 10.1155/2019/8479086
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    Cited by:

    1. Mohammed Benmoumen & Imane Salhi, 2023. "The Strong Consistency of Quasi-Maximum Likelihood Estimators for p-order Random Coefficient Autoregressive (RCA) Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 617-632, February.

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