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.
# 18-026/III (2018-03-21)
- Paolo Gorgi, VU Amsterdam; Siem Jan (S.J.) Koopman, VU Amsterdam; Tinbergen Institute, The Netherlands; Mengheng Li, VU Amsterdam
- Factor model, GAS model, Inflation forecasting, MIDAS, Score-driven model, Weighted maximum likelihood
- JEL codes: