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|z 9781513545653
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|a 1018-5941
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|a BD-DhAAL
|c BD-DhAAL
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|a Caselli, Francesca.
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|a Predictive Density Aggregation :
|b A Model for Global GDP Growth /
|c Francesca Caselli, Francesco Grigoli, Romain Lafarguette, Changchun Wang.
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|a Washington, D.C. :
|b International Monetary Fund,
|c 2020.
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|a 1 online resource (33 pages)
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|a IMF Working Papers
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|a <strong>Off-Campus Access:</strong> No User ID or Password Required
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|a <strong>On-Campus Access:</strong> No User ID or Password Required
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|a Electronic access restricted to authorized BRAC University faculty, staff and students
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|a 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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|a Mode of access: Internet
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|a Density Aggregation
|2 imf
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|a Density Evaluation
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|a GDP Growth
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|a Math Display
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|a WP
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|a United States
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|a Grigoli, Francesco.
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|a Lafarguette, Romain.
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|a Wang, Changchun.
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|a IMF Working Papers; Working Paper ;
|v No. 2020/078
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|z Full text available on IMF
|u http://elibrary.imf.org/view/journals/001/2020/078/001.2020.issue-078-en.xml
|z IMF e-Library
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