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Statistical Software for State Space Methods

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  • Jacques J. F. Commandeur
  • Siem Jan Koopman
  • Marius Ooms

Abstract

In this paper we review the state space approach to time series analysis and establish the notation that is adopted in this special volume of the Journal of Statistical Software. We first provide some background on the history of state space methods for the analysis of time series. This is followed by a concise overview of linear Gaussian state space analysis including the modelling framework and appropriate estimation methods. We discuss the important class of unobserved component models which incorporate a trend, a seasonal, a cycle, and fixed explanatory and intervention variables for the univariate and multivariate analysis of time series. We continue the discussion by presenting methods for the computation of different estimates for the unobserved state vector: filtering, prediction, and smoothing. Estimation approaches for the other parameters in the model are also considered. Next, we discuss how the estimation procedures can be used for constructing confidence intervals, detecting outlier observations and structural breaks, and testing model assumptions of residual independence, homoscedasticity, and normality. We then show how ARIMA and ARIMA components models fit in the state space framework to time series analysis. We also provide a basic introduction for non-Gaussian state space models. Finally, we present an overview of the software tools currently available for the analysis of time series with state space methods as they are discussed in the other contributions to this special volume.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Statistical Software.

Volume (Year): 41 ()
Issue (Month): i01 ()
Pages:

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Handle: RePEc:jss:jstsof:41:i01

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  1. Commandeur, Jacques J.F. & Koopman, Siem Jan, 2007. "An Introduction to State Space Time Series Analysis," OUP Catalogue, Oxford University Press, number 9780199228874.
  2. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543.
  3. Riccardo Lucchetti, . "State Space Methods in gretl," Journal of Statistical Software, American Statistical Association, vol. 41(i11).
  4. [Reference to Proietti], Tommaso, 2000. "Comparing seasonal components for structural time series models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 247-260.
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Cited by:
  1. Jyh-Ying Peng & John A. D. Aston, . "The State Space Models Toolbox for MATLAB," Journal of Statistical Software, American Statistical Association, vol. 41(i06).
  2. Roy Mendelssohn, . "The STAMP Software for State Space Models," Journal of Statistical Software, American Statistical Association, vol. 41(i02).
  3. Giovanni Petris & Sonia Petrone, . "State Space Models in R," Journal of Statistical Software, American Statistical Association, vol. 41(i04).
  4. William R. Bell, . "REGCMPNT : A Fortran Program for Regression Models with ARIMA Component Errors," Journal of Statistical Software, American Statistical Association, vol. 41(i07).
  5. Charles S. Bos, . "A Bayesian Analysis of Unobserved Component Models Using Ox," Journal of Statistical Software, American Statistical Association, vol. 41(i13).
  6. Riccardo Lucchetti, . "State Space Methods in gretl," Journal of Statistical Software, American Statistical Association, vol. 41(i11).
  7. Rajesh Selukar, . "State Space Modeling Using SAS," Journal of Statistical Software, American Statistical Association, vol. 41(i12).
  8. Zietz, Joachim & Traian, Anca, 2014. "When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 271-281.

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