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A low dimensional Kalman filter for systems with lagged observables

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  • Kristoffer Nimark

Abstract

This note describes how the Kalman filter can be modified to allow for the vector of observables to be a function of lagged variables without increasing the dimension of the state vector in the filter. This is useful in applications where it is desirable to keep the dimension of the state vector low. The modified filter and accompanying code (which nests the standard filter) can be used to compute (i) the steady state Kalman filter (ii) the log likelihood of a parameterized state space model conditional on a history of observables (iii) a smoothed estimate of latent state variables and (iv) a draw from the distribution of latent states conditional on a history of observables.

Suggested Citation

  • Kristoffer Nimark, 2009. "A low dimensional Kalman filter for systems with lagged observables," Economics Working Papers 1182, Department of Economics and Business, Universitat Pompeu Fabra.
    • Handle: RePEc:upf:upfgen:1182
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    Cited by:

    1. Leonardo Melosi, 2014. "Signaling Effects of Monteray Policy," 2014 Meeting Papers 830, Society for Economic Dynamics.
    2. John Barrdear, 2014. "Peering into the mist: social learning over an opaque observation network," Discussion Papers 1409, Centre for Macroeconomics (CFM).
    3. Leonardo Melosi, 2017. "Signalling Effects of Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 84(2), pages 853-884.
    4. Kristoffer Nimark, 2009. "Speculative dynamics in the term structure of interest rates," Economics Working Papers 1194, Department of Economics and Business, Universitat Pompeu Fabra, revised Sep 2012.

    More about this item

    Keywords

    Kalman filter; lagged observables; Kalman smoother; simulation smoother;
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