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A comparison between parallel algorithms for system parameter estimation in dynamic linear models
Author(s) -
Mantovan P.,
Pastore A.,
Tonellato S.
Publication year - 1999
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/(sici)1526-4025(199910/12)15:4<369::aid-asmb400>3.0.co;2-q
Subject(s) - estimator , consistency (knowledge bases) , algorithm , computer science , series (stratigraphy) , monte carlo method , estimation theory , estimation , maximum likelihood , linear model , mathematical optimization , mathematics , statistics , artificial intelligence , machine learning , paleontology , management , economics , biology
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.