Forecasting economic time series using score-driven dynamic models with mixed-data sampling

Paolo Gorgi (), Siem Jan Koopman and Mengheng Li
Additional contact information
Paolo Gorgi: VU Amsterdam

No 18-026/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: 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)
Date: 2018-03-21
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://papers.tinbergen.nl/18026.pdf (application/pdf)

Related works:
Journal Article: Forecasting economic time series using score-driven dynamic models with mixed-data sampling (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20180026

Access Statistics for this paper

More papers in Tinbergen Institute Discussion Papers from Tinbergen Institute Contact information at EDIRC.
Bibliographic data for series maintained by Tinbergen Office +31 (0)10-4088900 ().