Block Kalman Filtering for Large-Scale DSGE Models
AbstractIn 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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Bibliographic InfoArticle provided by Society for Computational Economics in its journal Computational Economics.
Volume (Year): 33 (2009)
Issue (Month): 3 (April)
Kalman filter; DSGE model; Bayesian estimation; Algorithm; Fortran; Matlab; C11; C13; C63;
Other versions of this item:
- Strid, Ingvar & Walentin, Karl, 2008. "Block Kalman filtering for large-scale DSGE models," Working Paper Series 224, Sveriges Riksbank (Central Bank of Sweden).
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
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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
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- Edward P. Herbst, 2012. "Using the "Chandrasekhar Recursions" for likelihood evaluation of DSGE models," Finance and Economics Discussion Series 2012-35, Board of Governors of the Federal Reserve System (U.S.).
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