Precision-based sampling for state space models that have no measurement error

Elmar Mertens

Journal of Economic Dynamics and Control, 2023, vol. 154, issue C

Abstract: This article presents a computationally efficient approach to sample from Gaussian state space models. The method is an instance of precision-based sampling methods that operate on the inverse variance-covariance matrix of the states (also known as precision). The novelty is to handle cases where the observables are modeled as a linear combination of the states without measurement error. In this case, the posterior variance of the states is singular and precision is ill-defined. As in other instances of precision-based sampling, computational gains are considerable. Relevant applications include trend-cycle decompositions, (mixed-frequency) VARs with missing variables and DSGE models.

Keywords: State space models; Signal extraction; Kalman filter and smoother; Precision-based sampling; Band matrix (search for similar items in EconPapers)
JEL-codes: C11 C32 C51 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:154:y:2023:i:c:s0165188923001264

DOI: 10.1016/j.jedc.2023.104720

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