Parameter space restrictions in state space models

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

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)109-123
Number of pages15
JournalJournal of Forecasting
Volume31
Issue number2
DOIs
StatePublished - Mar 2012

Fingerprint

State-space Model
Parameter Space
Restriction
Latent Process
Autocovariance
Moving Average Model
Positive Definiteness
Covariance Structure
Integrated Model
Statistical test
Time Series Data
Gross
Data-driven
Statistical tests
Cover
Time series
State-space model

Keywords

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

ASJC Scopus subject areas

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

Cite this

Parameter space restrictions in state space models. / Jun, Duk Bin; Kim, Dong Soo; Park, Sungho; Park, Myoung Hwan.

In: Journal of Forecasting, Vol. 31, No. 2, 03.2012, p. 109-123.

Research output: Contribution to journalArticle

Jun, Duk Bin ; Kim, Dong Soo ; Park, Sungho ; Park, Myoung Hwan. / Parameter space restrictions in state space models. In: Journal of Forecasting. 2012 ; Vol. 31, No. 2. pp. 109-123.
@article{747502d1368a40a6a9734a6f166cd229,
title = "Parameter space restrictions in state space models",
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.",
keywords = "ARIMA models, parameter space restrictions, state space models, trend-cycle decomposition",
author = "Jun, {Duk Bin} and Kim, {Dong Soo} and Sungho Park and Park, {Myoung Hwan}",
year = "2012",
month = "3",
doi = "10.1002/for.1209",
language = "English (US)",
volume = "31",
pages = "109--123",
journal = "Journal of Forecasting",
issn = "0277-6693",
publisher = "John Wiley and Sons Ltd",
number = "2",

}

TY - JOUR

T1 - Parameter space restrictions in state space models

AU - Jun, Duk Bin

AU - Kim, Dong Soo

AU - Park, Sungho

AU - Park, Myoung Hwan

PY - 2012/3

Y1 - 2012/3

N2 - 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.

AB - 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.

KW - ARIMA models

KW - parameter space restrictions

KW - state space models

KW - trend-cycle decomposition

UR - http://www.scopus.com/inward/record.url?scp=84856297083&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84856297083&partnerID=8YFLogxK

U2 - 10.1002/for.1209

DO - 10.1002/for.1209

M3 - Article

AN - SCOPUS:84856297083

VL - 31

SP - 109

EP - 123

JO - Journal of Forecasting

JF - Journal of Forecasting

SN - 0277-6693

IS - 2

ER -