Block Kalman filtering for large-scale DSGE models
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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- Adolfson, Malin & Laséen, Stefan & Lindé, Jesper & Villani, Mattias, 2005.
"Bayesian Estimation of an Open Economy DSGE Model with Incomplete Pass-Through,"
Working Paper Series
179, Sveriges Riksbank (Central Bank of Sweden).
- 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.
- 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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