A multivariate stochastic model with non‐stationary trend component
作者:
Hiroko Kato,
Sadao Naniwa,
Makio Ishiguro,
期刊:
Applied Stochastic Models and Data Analysis
(WILEY Available online 1995)
卷期:
Volume 11,
issue 1
页码: 77-95
ISSN:8755-0024
年代: 1995
DOI:10.1002/asm.3150110109
出版商: John Wiley&Sons, Ltd.
关键词: AIC;Bayesian model;co‐movements;non‐stationary stochastic time series model
数据来源: WILEY
摘要:
AbstractThe purposes of this paper are to introduce a multivariate non‐stationary stochastic time series model without individual detrending and to extract the multiple relationships between variables. To infer the statistical relation between variables, we attempt to estimate the co‐movement of multivariate non‐stationary time series components. The model is expressed in state‐space form, and time series components are estimated by the maximum likelihood method using numerical optimization algorithm. The Kalman filter algorithm is used to compute the likelihood of the model. The AIC procedure gives a criterion for selecting the best model fit for the data. The multiple relationship becomes clear by analysing estimated AR coefficients. Real economic data are used for a numerical
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