Arbeitspapier
Forecasting economic time series using score-driven dynamic models with mixed-data sampling
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. TI 2018-026/III
- Klassifikation
-
Wirtschaft
Classification Discontinued 2008. See C83.
- Thema
-
Factor model
GAS model
Inflation forecasting
MIDAS
Score-driven model
Weighted maximum likelihood
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Gorgi, Paolo
Koopman, Siem Jan
Li, Mengheng
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2018
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Gorgi, Paolo
- Koopman, Siem Jan
- Li, Mengheng
- Tinbergen Institute
Entstanden
- 2018
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