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Type | Working Paper |
Scope | Discipline-based scholarship |
Title | Particle Filtering, Learning, and Smoothing for Mixed-Frequency State-Space Models |
Organization Unit | |
Authors |
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Institution | University of Zurich |
Series Name | SSRN |
Number | 2856948 |
ISSN | 1556-5068 |
Date | 2018 |
Abstract Text | 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 inference 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. |
Other Identification Number | merlin-id:13966 |
PDF File | Download from ZORA |
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