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Constraining Kalman Filter and Smoothing Estimates to Satisfy Time-Varying Restrictions

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  • Doran, Howard E
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    Abstract

    It sometimes happens that the unobservable state vector of a linear dynamic model expressed in the state space is subject to known restrictions. Incorporation of this information into the Kalman filter procedure will increase the efficiency of estimation. It is shown that a simple augmentation of the measurement equation constrains the estimated state vector to obey the restrictions. The method applies whether the restrictions are time-invariant, time-varying, linear, or nonlinear. Copyright 1992 by MIT Press.

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    Bibliographic Info

    Article provided by MIT Press in its journal Review of Economics & Statistics.

    Volume (Year): 74 (1992)
    Issue (Month): 3 (August)
    Pages: 568-72

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    Handle: RePEc:tpr:restat:v:74:y:1992:i:3:p:568-72

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    Web page: http://mitpress.mit.edu/journals/

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    Web: http://mitpress.mit.edu/journal-home.tcl?issn=00346535

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    Cited by:
    1. Luati, Alessandra & Proietti, Tommaso, 2012. "Maximum likelihood estimation of time series models: the Kalman filter and beyond," Working Papers 02 BAWP, University of Sydney Business School, Discipline of Business Analytics.
    2. Andrle, Michal, 2012. "Understanding DSGE Filters in Forecasting and Policy Analysis," Dynare Working Papers 16, CEPREMAP.
    3. Pizzinga, Adrian, 2009. "Further investigation into restricted Kalman filtering," Statistics & Probability Letters, Elsevier, vol. 79(2), pages 264-269, January.
    4. Doran, Howard E. & Rambaldi, Alicia N., 1997. "Applying linear time-varying constraints to econometric models: With an application to demand systems," Journal of Econometrics, Elsevier, vol. 79(1), pages 83-95, July.
    5. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2013. "On the Stratonovich – Kalman - Bucy filtering algorithm application for accurate characterization of financial time series with use of state-space model by central banks," MPRA Paper 50235, University Library of Munich, Germany.

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