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

P. Mantovan, A. Pastore and S. Tonellato

Applied Stochastic Models in Business and Industry, 1999, vol. 15, issue 4, 369-378

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.

Date: 1999
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/(SICI)1526-4025(199910/12)15:43.0.CO;2-Q

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:15:y:1999:i:4:p:369-378

Access Statistics for this article

More articles in Applied Stochastic Models in Business and Industry from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().