Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/262020 
Authors: 
Year of Publication: 
2022
Series/Report no.: 
Volkswirtschaftliche Diskussionsreihe No. 343
Publisher: 
Universität Augsburg, Institut für Volkswirtschaftslehre, Augsburg
Abstract: 
We propose a modified version of the augmented Kalman filter (AKF) to evaluate the likelihood of linear and time-invariant state-space models (SSMs). Unlike the regular AKF, this augmented steady-state Kalman filter (ASKF), as we call it, is based on a steady-state Kalman filter (SKF). We show that to apply the ASKF, it is sufficient that the SSM at hand is stationary. We find that the ASKF can significantly reduce the computational burden to evaluate the likelihood of medium- to large-scale SSMs, making it particularly useful to estimate dynamic stochastic general equilibrium (DSGE) models and dynamic factor models. Tests using a medium-scale DSGE model, namely the 2007 version of the Smets and Wouters model, show that the ASKF is up to five times faster than the regular Kalman filter (KF). Other competing algorithms, such as the Chandrasekhar recursion (CR) or a univariate treatment of multivariate observation vectors (UKF), are also outperformed by the ASKF in terms of computational efficiency.
Subjects: 
Kalman filter
DSGE
Bayesian estimation
Maximum-likelihood estimation
Computational techniques
JEL: 
C18
C63
E20
Document Type: 
Working Paper

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