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

Paolo Gorgi, Siem Jan Koopman*, Mengheng Li

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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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 low-frequency time series variables through the use of timely information from high-frequency variables. We verify the in-sample and out-of-sample performances of the model in an empirical study on the forecasting of U.S. headline inflation and GDP growth. In particular, we forecast monthly headline inflation using daily oil prices and quarterly GDP growth using a measure of financial risk. 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 both point and density forecasts.

Original languageEnglish
Pages (from-to)1735-1747
Number of pages13
JournalInternational Journal of Forecasting
Issue number4
Early online date7 Jan 2019
Publication statusPublished - Oct 2019


  • Generalized autoregressive score models
  • Gross domestic product
  • Inflation
  • Mixed frequency time series
  • Time-varying parameters


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