Block Kalman filtering for large-scale DSGE models

Ingvar Strid () and Karl Walentin ()
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Ingvar Strid: Stockholm School of Economics, Postal: Stockholm School of Economics, Dept. of Economic Statistics and Decision Support, P.O. Box 6501, SE-113 83 Stockholm, Sweden

No 224, Working Paper Series from Sveriges Riksbank (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

Keywords: Kalman filter; DSGE model; Bayesian estimation; Computational speed; Algorithm; Fortran; Matlab (search for similar items in EconPapers)
JEL-codes: C10 C60 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge, nep-ecm, nep-ets and nep-ore
Date: Written 2008-06-01

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