Computational Economics

, Volume 33, Issue 3, pp 277–304

Block Kalman Filtering for Large-Scale DSGE Models

Authors

  • Ingvar Strid
    • Department of Economic Statistics and Decision SupportStockholm School of Economics
    • Research DepartmentSveriges Riksbank
Article

DOI: 10.1007/s10614-008-9160-4

Cite this article as:
Strid, I. & Walentin, K. Comput Econ (2009) 33: 277. doi:10.1007/s10614-008-9160-4

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 (J Time Ser Anal 21:281–296, 2000) and, furthermore, the two approaches are largely complementary.

Keywords

Kalman filterDSGE modelBayesian estimationAlgorithmFortranMatlab

JEL Codes

C11C13C63

Copyright information

© Springer Science+Business Media, LLC. 2008