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The State Space Models Toolbox for MATLAB

Author

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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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    Cited by:

    1. Niccolò Comerio & Fausto Pacicco & Massimiliano Serati, 2024. "“Fly down”: the impact of new accounting standards on the airline industry risk assessment," Empirical Economics, Springer, vol. 67(5), pages 2109-2133, November.
    2. Arora, Vipin & Lieskovsky, Jozef, 2016. "Electricity Use as an Indicator of U.S. Economic Activity," EconStor Research Reports 126147, ZBW - Leibniz Information Centre for Economics.
    3. 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.
    4. 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.
    5. 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).
    6. 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.).
    7. 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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