High-performance implementation of the level-3 BLAS
ACM Transactions on Mathematical Software (TOMS)
Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach
Computational Statistics & Data Analysis
Open government in policy development: from collaborative scenario texts to formal policy models
ICDCIT'11 Proceedings of the 7th international conference on Distributed computing and internet technology
Method and Tools to Support Stakeholder Engagement in Policy Development: The OCOPOMO Project
International Journal of Electronic Government Research
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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 (J Time Ser Anal 21:281---296, 2000) and, furthermore, the two approaches are largely complementary.