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 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 language | English |
|---|---|
| Journal | International Journal of Forecasting |
| DOIs | |
| Publication status | Accepted/In press - 1 Jan 2019 |
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Keywords
- Generalized autoregressive score models
- Gross domestic product
- Inflation
- Mixed frequency time series
- Time-varying parameters
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Forecasting economic time series using score-driven dynamic models with mixed-data sampling. / Gorgi, Paolo; Koopman, Siem Jan; Li, Mengheng.
In: International Journal of Forecasting, 01.01.2019.Research output: Contribution to Journal › Article › Academic › peer-review
TY - JOUR
T1 - Forecasting economic time series using score-driven dynamic models with mixed-data sampling
AU - Gorgi, Paolo
AU - Koopman, Siem Jan
AU - Li, Mengheng
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Generalized autoregressive score models
KW - Gross domestic product
KW - Inflation
KW - Mixed frequency time series
KW - Time-varying parameters
UR - http://www.scopus.com/inward/record.url?scp=85059463907&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059463907&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2018.11.005
DO - 10.1016/j.ijforecast.2018.11.005
M3 - Article
JO - International Journal of Forecasting
JF - International Journal of Forecasting
SN - 0169-2070
ER -