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

Elmar Mertens

No 25/2023, Discussion Papers from Deutsche Bundesbank

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
New Economics Papers: this item is included in nep-ecm and nep-ets
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Journal Article: Precision-based sampling for state space models that have no measurement error (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:bubdps:252023

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