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A New Recursive Estimation Method for Single Input Single Output Models
Author(s) -
Ouakasse Abdelhamid,
Mélard Guy
Publication year - 2017
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12210
Subject(s) - mathematics , estimator , hessian matrix , asymptotic distribution , consistency (knowledge bases) , monte carlo method , strong consistency , series (stratigraphy) , mathematical optimization , statistics , paleontology , geometry , biology
This article is devoted to a new recursive estimation method for dynamic time series models, more precisely for single input single output models. In that method, the recurrence for updating the Hessian is avoided, but the recurrence for updating the estimator makes use of the Fisher information matrix. The asymptotic properties, consistency and asymptotic normality, of the new estimator are obtained under weak assumptions. Monte Carlo experiments and examples indicate that the estimates converge well, comparatively with alternative methods.