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The State Space Models Toolbox for MATLAB

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  • Peng, Jyh-Ying
  • Aston, John A. D.

Abstract

State Space Models (SSM) is a MATLAB toolbox for time series analysis by state space methods. The software features fully interactive construction and combination of models, with support for univariate and multivariate models, complex time-varying (dy- namic) models, non-Gaussian models, and various standard models such as ARIMA and structural time-series models. The software includes standard functions for Kalman fil- tering and smoothing, simulation smoothing, likelihood evaluation, parameter estimation, signal extraction and forecasting, with incorporation of exact initialization for filters and smoothers, and support for missing observations and multiple time series input with com- mon analysis structure. The software also includes implementations of TRAMO model selection and Hillmer-Tiao decomposition for ARIMA models. The software will provide a general toolbox for time series analysis on the MATLAB platform, allowing users to take advantage of its readily available graph plotting and general matrix computation capabilities.

Suggested Citation

  • Peng, Jyh-Ying & Aston, John A. D., 2011. "The State Space Models Toolbox for MATLAB," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 41(i06).
    • Handle: RePEc:jss:jstsof:v:041:i06
    DOI: http://hdl.handle.net/10.18637/jss.v041.i06
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    References listed on IDEAS

    as
    1. Commandeur, Jacques J. F. & Koopman, Siem Jan & Ooms, Marius, 2011. "Statistical Software for State Space Methods," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 41(i01).
    2. Víctor Gómez & Agustín Maravall, 1998. "Seasonal Adjustment and Signal Extraction in Economic Time Series," Working Papers 9809, Banco de España.
    3. Víctor Gómez & Agustín Maravall, 1998. "Automatic Modeling Methods for Univariate Series," Working Papers 9808, Banco de España.
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    2. Pacicco, Fausto & Vena, Luigi & Venegoni, Andrea, 2020. "Communication and financial supervision: How does disclosure affect market stability?," Journal of Empirical Finance, Elsevier, vol. 57(C), pages 1-15.
    3. Hongsheng Bi & Rubao Ji & Hui Liu & Young-Heon Jo & Jonathan A Hare, 2014. "Decadal Changes in Zooplankton of the Northeast U.S. Continental Shelf," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-12, January.
    4. Fausto Pacicco & Luigi Vena & Andrea Venegoni, 2017. "Full disclosure and financial stability: how does the market digest the transparency shock?," LIUC Papers in Economics 305, Cattaneo University (LIUC).
    5. Ergys Islamaj & Maziar Kazemi, 2014. "Returns to Active Management: The Case of Hedge Funds," International Finance Discussion Papers 1112, Board of Governors of the Federal Reserve System (U.S.).
    6. Neha Saini & Anil Kumar Mittal, 2019. "On the predictive ability of GARCH and SV models of volatility: An empirical test on the SENSEX index," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 8(4), pages 1-5.

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