Parameter space restrictions in state space models

Duk Bin Jun, Dong Soo Kim, Sungho Park, Myoung Hwan Park

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)109-123
Number of pages15
JournalJournal of Forecasting
Issue number2
StatePublished - Mar 2012


  • ARIMA models
  • parameter space restrictions
  • state space models
  • trend-cycle decomposition

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications
  • Strategy and Management
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research


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