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On computing the expected Fisher information matrix for state-space model parameters

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  • Cavanaugh, Joseph E.
  • Shumway, Robert H.

Abstract

A general, recursive algorithm is presented for computing the expected Fisher information matrix for state-space model parameters. Simulation results are featured where known Fisher information matrices corresponding to simple state-space models are estimated using both observed and expected information matrices. The accuracy of the two approaches is compared.

Suggested Citation

  • Cavanaugh, Joseph E. & Shumway, Robert H., 1996. "On computing the expected Fisher information matrix for state-space model parameters," Statistics & Probability Letters, Elsevier, vol. 26(4), pages 347-355, March.
    • Handle: RePEc:eee:stapro:v:26:y:1996:i:4:p:347-355
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    References listed on IDEAS

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    1. N. Watanabe, 1985. "Note On The Kalman Filter With Estimated Parameters," Journal of Time Series Analysis, Wiley Blackwell, vol. 6(4), pages 269-278, July.
    2. R. H. Shumway & D. S. Stoffer, 1982. "An Approach To Time Series Smoothing And Forecasting Using The Em Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 3(4), pages 253-264, July.
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    1. Arno Strouwen & Bart M. Nicolaï & Peter Goos, 2023. "Adaptive and robust experimental design for linear dynamical models using Kalman filter," Statistical Papers, Springer, vol. 64(4), pages 1209-1231, August.
    2. Papanastassiou, Demetrios, 2006. "Computing the covariance matrix of QML estimators for a state space model," Statistics & Probability Letters, Elsevier, vol. 76(10), pages 1001-1006, May.
    3. Alonso Fernández, Andrés Modesto & García-Martos, Carolina & Rodríguez, Julio & Sánchez, María Jesús, 2008. "Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting," DES - Working Papers. Statistics and Econometrics. WS ws081406, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Dani Gamerman & Thiago Rezende Santos & Glaura C. Franco, 2013. "A Non-Gaussian Family Of State-Space Models With Exact Marginal Likelihood," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(6), pages 625-645, November.
    5. Tobias Hartl & Roland Jucknewitz, 2022. "Approximate state space modelling of unobserved fractional components," Econometric Reviews, Taylor & Francis Journals, vol. 41(1), pages 75-98, January.
    6. Alexander Tsyplakov, 2011. "An introduction to state space modeling (in Russian)," Quantile, Quantile, issue 9, pages 1-24, July.
    7. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    8. Giampiero Marra & Rosalba Radice & Silvia Missiroli, 2014. "Testing the hypothesis of absence of unobserved confounding in semiparametric bivariate probit models," Computational Statistics, Springer, vol. 29(3), pages 715-741, June.

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