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Block Kalman filtering for large-scale DSGE models

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  • Strid, Ingvar

    ()
    (Stockholm School of Economics)

  • Walentin, Karl

    ()
    (Research Department, Central Bank of Sweden)

Abstract

In this paper block Kalman filters for Dynamic Stochastic General Equilibrium models are presented and evaluated. Our approach is based on the simple idea of writing down the Kalman filter recursions on block form and appropriately sequencing the operations of the prediction step of the algorithm. It is argued that block filtering is the only viable serial algorithmic approach to significantly reduce Kalman filtering time in the context of large DSGE models. For the largest model we evaluate the block filter reduces the computation time by roughly a factor 2. Block filtering compares favourably with the more general method for faster Kalman filtering outlined by Koopman and Durbin (2000) and, furthermore, the two approaches are largely complementary

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Bibliographic Info

Paper provided by Sveriges Riksbank (Central Bank of Sweden) in its series Working Paper Series with number 224.

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Length: 34 pages
Date of creation: 01 Jun 2008
Date of revision:
Handle: RePEc:hhs:rbnkwp:0224

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Related research

Keywords: Kalman filter; DSGE model; Bayesian estimation; Computational speed; Algorithm; Fortran; Matlab;

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References

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  1. Koopman, S.J.M. & Durbin, J., 1998. "Fast Filtering and Smoothing for Multivariate State Space Models," Discussion Paper, Tilburg University, Center for Economic Research 1998-18, Tilburg University, Center for Economic Research.
  2. Ivano Azzini & Riccardo Girardi & Marco Ratto, 2007. "Parallelization of Matlab codes under Windows platform for Bayesian estimation: A Dynare application," Working Papers, Euro-area Economy Modelling Centre 1, Euro-area Economy Modelling Centre.
  3. Adolfson, Malin & Laséen, Stefan & Lindé, Jesper & Villani, Mattias, 2005. "Bayesian Estimation of an Open Economy DSGE Model with Incomplete Pass-Through," Working Paper Series, Sveriges Riksbank (Central Bank of Sweden) 179, Sveriges Riksbank (Central Bank of Sweden).
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Cited by:
  1. Strid, Ingvar, 2010. "Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 54(11), pages 2814-2835, November.
  2. Edward P. Herbst, 2012. "Using the "Chandrasekhar Recursions" for likelihood evaluation of DSGE models," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 2012-35, Board of Governors of the Federal Reserve System (U.S.).
  3. Strid, Ingvar, 2008. "Metropolis-Hastings prefetching algorithms," Working Paper Series in Economics and Finance, Stockholm School of Economics 706, Stockholm School of Economics, revised 02 Dec 2009.

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