Scandinavian Working Papers in Economics
Working Paper Series,
Sveriges Riksbank (Central Bank of Sweden)
No 224: Block Kalman filtering for large-scale DSGE models
Ingvar Strid () and Karl Walentin ()
Additional contact information
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
Karl Walentin: Research Department, Central Bank of Sweden, Postal: Sveriges Riksbank, SE-103 37 Stockholm, 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
34 pages, June 1, 2008
Full text files
wp224ny.pdf
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