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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Particle Filtering, Learning, and Smoothing for Mixed-Frequency State-Space Models |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Econometrics and Statistics |
| Publisher | Elsevier |
| Geographical Reach | international |
| ISSN | 2468-0389 |
| Volume | 12 |
| Page Range | 25 - 41 |
| Date | 2019 |
| Abstract Text | 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 stateaugmented 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. |
| Related URLs | |
| Digital Object Identifier | 10.1016/j.ecosta.2019.07.001 |
| Other Identification Number | merlin-id:17992 |
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