Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/177716 
Erscheinungsjahr: 
2018
Schriftenreihe/Nr.: 
Tinbergen Institute Discussion Paper No. TI 2018-026/III
Verlag: 
Tinbergen Institute, Amsterdam and Rotterdam
Zusammenfassung: 
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.
Schlagwörter: 
Factor model
GAS model
Inflation forecasting
MIDAS
Score-driven model
Weighted maximum likelihood
JEL: 
C42
Dokumentart: 
Working Paper
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