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Particle Filtering, Learning, and Smoothing for Mixed-Frequency State-Space Models


Yang, Hanlin; Leippold, Markus (2016). Particle Filtering, Learning, and Smoothing for Mixed-Frequency State-Space Models. , University of Zurich.

Abstract

We propose a general particle filtering and learning framework for mixed-frequency state-space models. Our mixed-frequency particle methods use a smoother so as to draw the Bayesian in- ference from low-frequency observations. Our forward smoother is simple and efficient, and the sample path degeneracy is negligible with a small lag size. The backward smoother mitigates the sample path degeneracy effect with quadratic computations that are nevertheless parallelizable. To illustrate our mixed-frequency particle framework, we take the mixed-frequency conditional dynamic linear model with regime switching as an example. In a simulation study, we show that naive treatments of mixed frequencies may severely impact model identification.

Abstract

We propose a general particle filtering and learning framework for mixed-frequency state-space models. Our mixed-frequency particle methods use a smoother so as to draw the Bayesian in- ference from low-frequency observations. Our forward smoother is simple and efficient, and the sample path degeneracy is negligible with a small lag size. The backward smoother mitigates the sample path degeneracy effect with quadratic computations that are nevertheless parallelizable. To illustrate our mixed-frequency particle framework, we take the mixed-frequency conditional dynamic linear model with regime switching as an example. In a simulation study, we show that naive treatments of mixed frequencies may severely impact model identification.

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Additional indexing

Item Type:Working Paper
Communities & Collections:03 Faculty of Economics > Department of Banking and Finance
Dewey Decimal Classification:330 Economics
Language:English
Date:25 October 2016
Deposited On:15 Nov 2016 13:48
Last Modified:02 Feb 2018 10:40
OA Status:Green
Other Identification Number:merlin-id:13966

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