Does joint modelling of the world economy pay off?: evaluating global forecasts from a Bayesian GVAR
We analyze how modeling international dependencies improves forecasts for the global economy based on a Bayesian GVAR with SSVS prior and stochastic volatility. To analyze the source of performance gains, we decompose the predictive joint density into its marginals and a copula term capturing the de...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
5 July 2016
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| In: |
Journal of economic dynamics & control
Year: 2016, Volume: 70, Pages: 86-100 |
| ISSN: | 0165-1889 |
| DOI: | 10.1016/j.jedc.2016.06.006 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.jedc.2016.06.006 Verlag, lizenzpflichtig, Volltext: http://www.sciencedirect.com/science/article/pii/S0165188916301051 
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| Author Notes: | Jonas Dovern, Martin Feldkircher, Florian Huber |
| Summary: | We analyze how modeling international dependencies improves forecasts for the global economy based on a Bayesian GVAR with SSVS prior and stochastic volatility. To analyze the source of performance gains, we decompose the predictive joint density into its marginals and a copula term capturing the dependence structure across countries. The GVAR outperforms forecasts based on country-specific models. This performance is solely driven by superior predictions for the dependence structure across countries, whereas the GVAR does not yield better predictive marginal densities. The relative performance gains of the GVAR model are particularly pronounced during volatile periods and for emerging economies. |
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| Item Description: | Gesehen am 27.04.2020 |
| Physical Description: | Online Resource |
| ISSN: | 0165-1889 |
| DOI: | 10.1016/j.jedc.2016.06.006 |