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

39 Pages Posted: 22 Oct 2016 Last revised: 18 Jan 2021

See all articles by Markus Leippold

Markus Leippold

University of Zurich - Department of Banking and Finance; University of Zurich - Faculty of Economics, Business Administration and Information Technology

Hanlin Yang

University of Zurich

Date Written: August 1, 2019

Abstract

A particle filter approach for general mixed-frequency state-space models is considered. It employs a backward smoother to filter high-frequency state variables from low-frequency observations. Moreover, it preserves the sequential nature of particle filters, allows for non-Gaussian shocks and nonlinear state-measurement relation, and alleviates the concern over sample degeneracy. Simulation studies show that it outperforms the commonly used state-augmented approach for mixed-frequency data for filtering and smoothing. In an empirical exercise, predictive mixed-frequency regressions are employed for Treasury bond and US dollar index returns with quarterly predictors and monthly stochastic volatility. Stochastic volatility improves model inference and forecasting power in a mixed-frequency setup but not for quarterly aggregate models.

Keywords: Mixed-frequency, State-space Models, Particle Filtering, Particle Learning, Smoothing, Parameter Estimation, Real-time Learning, Confounded Learning

JEL Classification: C13, C32, C53

Suggested Citation

Leippold, Markus and Yang, Hanlin, Particle Filtering, Learning, and Smoothing for Mixed-Frequency State-Space Models (August 1, 2019). Available at SSRN: https://ssrn.com/abstract=2856948 or http://dx.doi.org/10.2139/ssrn.2856948

Markus Leippold (Contact Author)

University of Zurich - Department of Banking and Finance ( email )

Plattenstrasse 14
Zürich, 8032
Switzerland

University of Zurich - Faculty of Economics, Business Administration and Information Technology ( email )

Plattenstrasse 14
Zürich, 8032
Switzerland

Hanlin Yang

University of Zurich ( email )

Plattenstrasse 14
Zürich, 8032
Switzerland

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