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A comparison between parallel algorithms for system parameter estimation in dynamic linear models

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  • P. Mantovan
  • A. Pastore
  • S. Tonellato

Abstract

When dealing with high‐frequency time series, statistical procedures giving reliable estimates of unknown parameters and forecasts in real time are required. This is why recursive estimation methods are usually preferred to maximum‐likelihood estimators. In the paper, a recursive estimation algorithm for the system parameter of dynamic linear models is proposed. A comparison with some other algorithms is given via Monte Carlo simulations. Consistency properties of the algorithms are also empirically verified. Copyright © 1999 John Wiley & Sons, Ltd.

Suggested Citation

  • P. Mantovan & A. Pastore & S. Tonellato, 1999. "A comparison between parallel algorithms for system parameter estimation in dynamic linear models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 15(4), pages 369-378, October.
    • Handle: RePEc:wly:apsmbi:v:15:y:1999:i:4:p:369-378
    DOI: 10.1002/(SICI)1526-4025(199910/12)15:43.0.CO;2-Q
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