Block Kalman filtering for large-scale DSGE models

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Sveriges Riksbank Working Paper Series ; No. 224

Classification and Topics

• Classification: Wirtschaft
• Subject: Dynamisches Gleichgewicht ; Zustandsraummodell

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Strid, Ingvar ; Walentin, Karl
• Event: Veröffentlichung
• (who): Sveriges Riksbank
• (where): Stockholm
• (when): 2008

Further information

• Handle: http://hdl.handle.net/10419/81929
• Last update: 10.03.2025, 11:44 AM CET