Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian vector autoregressions?
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Wiley Open Access
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| Autores principales: | , , , |
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| Formato: | Artículo Open Access |
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Wiley
2024
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| _version_ | 1867006460051849217 |
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| author | Martin Feldkircher Luis Gruber Florian Huber Gregor Kastner |
| author_facet | Martin Feldkircher Luis Gruber Florian Huber Gregor Kastner Martin Feldkircher Luis Gruber Florian Huber Gregor Kastner |
| collection | Wiley Open Access |
| contents | Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian vector autoregressions? Martin Feldkircher Luis Gruber Florian Huber Gregor Kastner Journal of Forecasting AbstractWe assess the relationship between model size and complexity in the time‐varying parameter vector autoregression (VAR) framework via thorough predictive exercises for the euro area, the United Kingdom, and the United States. It turns out that sophisticated dynamics through drifting coefficients are important in small data sets, while simpler models tend to perform better in sizeable data sets. To combine the best of both worlds, novel shrinkage priors help to mitigate the curse of dimensionality, resulting in competitive forecasts for all scenarios considered. Furthermore, we discuss dynamic model selection to improve upon the best performing individual model for each point in time. 10.1002/for.3121 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/for.3121 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_for_3121 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian vector autoregressions? Martin Feldkircher Luis Gruber Florian Huber Gregor Kastner Journal of Forecasting Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian vector autoregressions? Martin Feldkircher Luis Gruber Florian Huber Gregor Kastner Journal of Forecasting AbstractWe assess the relationship between model size and complexity in the time‐varying parameter vector autoregression (VAR) framework via thorough predictive exercises for the euro area, the United Kingdom, and the United States. It turns out that sophisticated dynamics through drifting coefficients are important in small data sets, while simpler models tend to perform better in sizeable data sets. To combine the best of both worlds, novel shrinkage priors help to mitigate the curse of dimensionality, resulting in competitive forecasts for all scenarios considered. Furthermore, we discuss dynamic model selection to improve upon the best performing individual model for each point in time. 10.1002/for.3121 http://creativecommons.org/licenses/by/4.0/ |
| title | Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian vector autoregressions? |
| topic | Journal of Forecasting |
| url | https://onlinelibrary.wiley.com/doi/10.1002/for.3121 |