Parameter Space Restrictions in State Space Models |
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| Authors: | Duk Bin Jun Dong Soo Kim Sungho Park Myoung Hwan Park |
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| Institution: | 1. Department of Management Engineering, KAIST, Seoul, South Korea;2. W. P. Carey School of Business, Arizona State University, Tempe, Arizona, USA;3. Department of Industrial and Management Engineering, Hansung University, Seoul, South Korea |
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| Abstract: | 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 © 2011 John Wiley & Sons, Ltd. |
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| Keywords: | state space models ARIMA models parameter space restrictions trend– cycle decomposition |
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