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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Particle Filtering, Learning, and Smoothing for Mixed-Frequency State-Space Models
Organization Unit
  • Hanlin Yang
  • Markus Leippold
  • English
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
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