Forecasting economic time series using score-driven dynamic models with mixed-data sampling
Paolo Gorgi (),
Siem Jan Koopman () and
Mengheng Li ()
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Paolo Gorgi: VU Amsterdam
No 18-026/III, Tinbergen Institute Discussion Papers from Tinbergen Institute
We introduce a mixed-frequency score-driven dynamic model for multiple time series where the score contributions from high-frequency variables are transformed by means of a mixed-data sampling weighting scheme. The resulting dynamic model delivers a flexible and easy-to-implement framework for the forecasting of a low-frequency time series variable through the use of timely information from high-frequency variables. We aim to verify in-sample and out-of-sample performances of the model in an empirical study on the forecasting of U.S.~headline inflation. In particular, we forecast monthly inflation using daily oil prices and quarterly inflation using effective federal funds rates. The forecasting results and other findings are promising. Our proposed score-driven dynamic model with mixed-data sampling weighting outperforms competing models in terms of point and density forecasts.
Keywords: Factor model; GAS model; Inflation forecasting; MIDAS; Score-driven model; Weighted maximum likelihood (search for similar items in EconPapers)
JEL-codes: C42 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Journal Article: Forecasting economic time series using score-driven dynamic models with mixed-data sampling (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20180026
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