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• W3022272210 endingPage "41" @default.
• W3022272210 startingPage "25" @default.
• W3022272210 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." @default.
• W3022272210 created "2020-05-13" @default.
• W3022272210 creator A5073309846 @default.
• W3022272210 creator A5077315939 @default.
• W3022272210 date "2019-10-01" @default.
• W3022272210 modified "2023-10-17" @default.
• W3022272210 title "Particle filtering, learning, and smoothing for mixed-frequency state-space models" @default.
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• W3022272210 doi "https://doi.org/10.1016/j.ecosta.2019.07.001" @default.
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