Parameter Space Restrictions in State Space Models

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The state space model is widely used to handle time series data driven by related latent processes in many fields. In this article, we suggest a framework to examine the relationship between state space models and autoregressive integrated moving average (ARIMA) models by examining the existence and positive-definiteness conditions implied by auto-covariance structures. This study covers broad types of state space models frequently used in previous studies. We also suggest a simple statistical test to check whether a certain state space model is appropriate for the specific data. For illustration, we apply the suggested procedure in the analysis of the United States real gross domestic product data. Copyright (C) 2011 John Wiley & Sons, Ltd.
Publisher
WILEY-BLACKWELL
Issue Date
2012-03
Language
English
Article Type
Article
Keywords

MACROECONOMIC TIME-SERIES; BEVERIDGE-NELSON; DECOMPOSITION; COMPONENTS; CYCLE; TRENDS; PERMANENT

Citation

JOURNAL OF FORECASTING, v.31, no.2, pp.109 - 123

ISSN
0277-6693
DOI
10.1002/for.1209
URI
http://hdl.handle.net/10203/97742
Appears in Collection
MT-Journal Papers(저널논문)
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