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Block Kalman Filtering for Large-Scale DSGE Models

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  • Ingvar Strid

    ()

  • Karl Walentin

    ()

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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File URL: http://hdl.handle.net/10.1007/s10614-008-9160-4
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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 33 (2009)
Issue (Month): 3 (April)
Pages: 277-304

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Handle: RePEc:kap:compec:v:33:y:2009:i:3:p:277-304

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Web page: http://www.springerlink.com/link.asp?id=100248
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Keywords: Kalman filter; DSGE model; Bayesian estimation; Algorithm; Fortran; Matlab; C11; C13; C63;

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  1. Koopman, S.J.M. & Durbin, J., 1998. "Fast Filtering and Smoothing for Multivariate State Space Models," Discussion Paper 1998-18, Tilburg University, Center for Economic Research.
  2. Adolfson, Malin & Laseen, Stefan & Linde, Jesper & Villani, Mattias, 2007. "Bayesian estimation of an open economy DSGE model with incomplete pass-through," Journal of International Economics, Elsevier, vol. 72(2), pages 481-511, July.
  3. Ivano Azzini & Riccardo Girardi & Marco Ratto, 2007. "Parallelization of Matlab codes under Windows platform for Bayesian estimation: A Dynare application," Working Papers 1, Euro-area Economy Modelling Centre.
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Cited by:
  1. Edward P. Herbst, 2012. "Using the "Chandrasekhar Recursions" for likelihood evaluation of DSGE models," Finance and Economics Discussion Series 2012-35, Board of Governors of the Federal Reserve System (U.S.).
  2. Strid, Ingvar, 2010. "Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2814-2835, November.
  3. Strid, Ingvar, 2008. "Metropolis-Hastings prefetching algorithms," Working Paper Series in Economics and Finance 706, Stockholm School of Economics, revised 02 Dec 2009.

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