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An algorithm for estimating parameters of state-space models

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  • Wu, Lilian Shiao-Yen
  • Pai, Jeffrey S.
  • Hosking, J.R.M.
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    Abstract

    We describe an algorithm for estimating the parameters of time-series models expressed in state-space form. The algorithm is based on the EM algorithm, and generalizes an algorithm given by Shumway and Stoffer (1982)

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    Bibliographic Info

    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 28 (1996)
    Issue (Month): 2 (June)
    Pages: 99-106

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    Handle: RePEc:eee:stapro:v:28:y:1996:i:2:p:99-106

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    Related research

    Keywords: EM algorithm Kaiman filter Maximum likelihood Time series;

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    Cited by:
    1. Giuseppe Storti & Alessandra Amendola, 2000. "A Non Linear Time Series Approach To Modelling Asymmetry In Stock Market Indexes," Computing in Economics and Finance 2000 97, Society for Computational Economics.
    2. andrés M. Alonso & Carolina Garcia-Martos & Julio Rodriguez & Maria Jesus Sanchez, 2008. "Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting," Statistics and Econometrics Working Papers ws081406, Universidad Carlos III, Departamento de Estadística y Econometría.
    3. Venter, J.H. & de Jongh, P.J., 2014. "Extended stochastic volatility models incorporating realised measures," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 687-707.
    4. Lasse Bork, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," CREATES Research Papers 2009-11, School of Economics and Management, University of Aarhus.

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