Predictive Density Aggregation : A Model for Global GDP Growth /
In this paper we propose a novel approach to obtain the predictive density of global GDP growth. It hinges upon a bottom-up probabilistic model that estimates and combines single countries' predictive GDP growth densities, taking into account cross-country interdependencies. Speci?cally, we mod...
| Главный автор: | |
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| Другие авторы: | , , |
| Формат: | Журнал |
| Язык: | English |
| Опубликовано: |
Washington, D.C. :
International Monetary Fund,
2020.
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| Серии: | IMF Working Papers; Working Paper ;
No. 2020/078 |
| Предметы: | |
| Online-ссылка: | Full text available on IMF |
| Итог: | In this paper we propose a novel approach to obtain the predictive density of global GDP growth. It hinges upon a bottom-up probabilistic model that estimates and combines single countries' predictive GDP growth densities, taking into account cross-country interdependencies. Speci?cally, we model non-parametrically the contemporaneous interdependencies across the United States, the euro area, and China via a conditional kernel density estimation of a joint distribution. Then, we characterize the potential ampli?cation e?ects stemming from other large economies in each region-also with kernel density estimations-and the reaction of all other economies with para-metric assumptions. Importantly, each economy's predictive density also depends on a set of observable country-speci?c factors. Finally, the use of sampling techniques allows us to aggregate individual countries' densities into a world aggregate while preserving the non-i.i.d. nature of the global GDP growth distribution. Out-of-sample metrics con?rm the accuracy of our approach. |
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| Примечание: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| Объем: | 1 online resource (33 pages) |
| Формат: | Mode of access: Internet |
| ISSN: | 1018-5941 |
| Доступ: | Electronic access restricted to authorized BRAC University faculty, staff and students |