Particle filtering, learning, and smoothing for mixed-frequency state-space models

Markus Leippold () and Hanlin Yang

Econometrics and Statistics, 2019, vol. 12, issue C, 25-41

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; Backward smoothing; Stochastic volatility; Return predictability (search for similar items in EconPapers)
JEL-codes: C13 C32 C53 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:12:y:2019:i:c:p:25-41

DOI: 10.1016/j.ecosta.2019.07.001

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