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Kalman filter for singular and conditional state-space models when the system state and the observational error are correlated

Author

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  • Nieto, Fabio H.
  • Guerrero, Victor M.

Abstract

In this paper we derive the Kalman filter equations when the system state and the observation error of a state-space model are correlated. We also consider: (i) error processes with nonnegative definite variance-covariance matrices and (ii) disturbance probability distributions that are conditional on some information related to the observation process.

Suggested Citation

  • Nieto, Fabio H. & Guerrero, Victor M., 1995. "Kalman filter for singular and conditional state-space models when the system state and the observational error are correlated," Statistics & Probability Letters, Elsevier, vol. 22(4), pages 303-310, March.
    • Handle: RePEc:eee:stapro:v:22:y:1995:i:4:p:303-310
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    Cited by:

    1. Gomez, Nicolas & Guerrero, Victor M., 2006. "Restricted forecasting with VAR models: An analysis of a test for joint compatibility between restrictions and forecasts," International Journal of Forecasting, Elsevier, vol. 22(4), pages 751-770.
    2. Ivonne Caridad Perez Correa & Juan Miguel Martinez Buendia, 2013. "Desagregación multivariada del PIB sectorial del departamento de Bolívar," Revista Economía y Región, Universidad Tecnológica de Bolívar, vol. 7(1), pages 139-167, June.
    3. Jammalamadaka, S. Rao & Sengupta, D., 2007. "Inclusion and exclusion of data or parameters in the general linear model," Statistics & Probability Letters, Elsevier, vol. 77(12), pages 1235-1247, July.
    4. Fabio H. Nieto, 2007. "Ex post and ex ante prediction of unobserved multivariate time series: a structural-model based approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(1), pages 53-76.
    5. Ruiz, Edilberto & Nieto, Fabio H., 2000. "A note on linear combination of predictors," Statistics & Probability Letters, Elsevier, vol. 47(4), pages 351-356, May.

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