Nowcasting aggregate services trade:
The increasing importance of services trade in the global economy contrasts with the lack of timely data to monitor recent developments. The nowcasting models developed in this paper are aimed at providing insights into current changes in total services trade, as recorded in monthly statistics of th...
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Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
Paris
OECD Publishing
2021
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Schriftenreihe: | OECD Trade Policy Papers
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Schlagworte: | |
Online-Zugang: | Volltext |
Zusammenfassung: | The increasing importance of services trade in the global economy contrasts with the lack of timely data to monitor recent developments. The nowcasting models developed in this paper are aimed at providing insights into current changes in total services trade, as recorded in monthly statistics of the G7 countries. Combining machine-learning techniques and dynamic factor models, the methodology exploits traditional data and Google Trends search data. No single model outperforms the others, but a weighted average of the best models combining machine-learning with dynamic factor models seems to be a promising avenue. The best models improve one-step ahead predictive performance relative to a simple benchmark by 30-35% on average across G7 countries and trade flows. Nowcasting models are estimated to have captured about 67% of the fall in services exports due to the COVID-19 shock and 60% of the fall in imports on average across G7 economies |
Beschreibung: | 1 Online-Ressource (55 Seiten) |
DOI: | 10.1787/0ad7d27c-en |
Internformat
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Datensatz im Suchindex
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format | Electronic eBook |
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institution | BVB |
language | English |
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physical | 1 Online-Ressource (55 Seiten) |
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spelling | Jaax, Alexander Verfasser aut Nowcasting aggregate services trade Alexander Jaax, Frédéric Gonzales and Annabelle Mourougane Paris OECD Publishing 2021 1 Online-Ressource (55 Seiten) txt rdacontent c rdamedia cr rdacarrier OECD Trade Policy Papers The increasing importance of services trade in the global economy contrasts with the lack of timely data to monitor recent developments. The nowcasting models developed in this paper are aimed at providing insights into current changes in total services trade, as recorded in monthly statistics of the G7 countries. Combining machine-learning techniques and dynamic factor models, the methodology exploits traditional data and Google Trends search data. No single model outperforms the others, but a weighted average of the best models combining machine-learning with dynamic factor models seems to be a promising avenue. The best models improve one-step ahead predictive performance relative to a simple benchmark by 30-35% on average across G7 countries and trade flows. Nowcasting models are estimated to have captured about 67% of the fall in services exports due to the COVID-19 shock and 60% of the fall in imports on average across G7 economies Trade Gonzales, Frédéric ctb Mourougane, Annabelle ctb https://doi.org/10.1787/0ad7d27c-en Verlag kostenfrei Volltext |
spellingShingle | Jaax, Alexander Nowcasting aggregate services trade Trade |
title | Nowcasting aggregate services trade |
title_auth | Nowcasting aggregate services trade |
title_exact_search | Nowcasting aggregate services trade |
title_exact_search_txtP | Nowcasting aggregate services trade |
title_full | Nowcasting aggregate services trade Alexander Jaax, Frédéric Gonzales and Annabelle Mourougane |
title_fullStr | Nowcasting aggregate services trade Alexander Jaax, Frédéric Gonzales and Annabelle Mourougane |
title_full_unstemmed | Nowcasting aggregate services trade Alexander Jaax, Frédéric Gonzales and Annabelle Mourougane |
title_short | Nowcasting aggregate services trade |
title_sort | nowcasting aggregate services trade |
topic | Trade |
topic_facet | Trade |
url | https://doi.org/10.1787/0ad7d27c-en |
work_keys_str_mv | AT jaaxalexander nowcastingaggregateservicestrade AT gonzalesfrederic nowcastingaggregateservicestrade AT mourouganeannabelle nowcastingaggregateservicestrade |