Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/276235 
Authors: 
Year of Publication: 
2023
Series/Report no.: 
Deutsche Bundesbank Discussion Paper No. 25/2023
Publisher: 
Deutsche Bundesbank, Frankfurt a. M.
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.
Subjects: 
State space models
signal extraction
Kalman filter and smoother
precision-based sampling
band matrix
JEL: 
C11
C32
C51
ISBN: 
978-3-95729-956-7
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.