An Augmented Steady-State Kalman Filter to Evaluate the Likelihood of Linear and Time-Invariant State-Space Models

Johannes Huber

No 343, Discussion Paper Series from Universitaet Augsburg, Institute for Economics

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.

Keywords: kalman filter; dsge; bayesian estimation; maximum-likelihood estimation; computational techniques (search for similar items in EconPapers)
JEL-codes: C18 C63 E20 (search for similar items in EconPapers)
Date: 2022-04
New Economics Papers: this item is included in nep-dge, nep-ecm, nep-ets, nep-mac and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:aug:augsbe:0343

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