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Exact Score for Time Series Models in State Space Form (Now published in Biometrika (1992), 79, 4, pp.283-6.)

Author

Listed:
  • Siem Jan Koopman
  • N.G. Shephard

Abstract

The score vector for a time series model which fits into the Gaussian state space form can be approximated by numerically differentiating the log-likelihood. If the parameter vector is of length p, this involves the running of p + 1 Kalman filters. This paper shows the score vector can be computed in a single pass of the Kalman filter and a smoother. For many classes of models this dramatically increases the speed and reliability of algorithms for the numerical maximisation of likelihood.

Suggested Citation

  • Siem Jan Koopman & N.G. Shephard, 1992. "Exact Score for Time Series Models in State Space Form (Now published in Biometrika (1992), 79, 4, pp.283-6.)," STICERD - Econometrics Paper Series 241, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    • Handle: RePEc:cep:stiecm:241
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    Citations

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    Cited by:

    1. Borus Jungbacker & Siem Jan Koopman & Michel Wel, 2014. "Smooth Dynamic Factor Analysis With Application To The Us Term Structure Of Interest Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 65-90, January.
    2. Lasse Bork, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," CREATES Research Papers 2009-11, Department of Economics and Business Economics, Aarhus University.
    3. Neil Shephard, "undated". "The relationship between the conditional sum of squares and the exact likelihood for autoregressive moving average model," Economics Papers 1997-W6., Economics Group, Nuffield College, University of Oxford.
    4. F. Butter & S. Koopman, 2001. "Interaction between structural and cyclical shocks in production and employment," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 137(2), pages 273-296, June.

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