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A low dimensional Kalman filter for systems with lagged observables

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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.

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Bibliographic Info

Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 1182.

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Date of creation: Nov 2009
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Handle: RePEc:upf:upfgen:1182

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Web page: http://www.econ.upf.edu/

Related research

Keywords: Kalman filter; lagged observables; Kalman smoother; simulation smoother;

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Cited by:
  1. Kristoffer Nimark, 2009. "Speculative dynamics in the term structure of interest rates," Economics Working Papers, Department of Economics and Business, Universitat Pompeu Fabra 1194, Department of Economics and Business, Universitat Pompeu Fabra, revised Sep 2012.
  2. Leonardo Melosi, 2012. "Signaling effects of monetary policy," Working Paper Series, Federal Reserve Bank of Chicago WP-2012-05, Federal Reserve Bank of Chicago.

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