复杂和小规模与简单和大规模:在贝叶斯向量自回归中引入漂移系数何时会有回报?

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-03-11 DOI:10.1002/for.3121
Martin Feldkircher, Luis Gruber, Florian Huber, Gregor Kastner
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引用次数: 0

摘要

我们通过对欧元区、英国和美国的全面预测,评估了时变参数向量自回归(VAR)框架中模型规模与复杂性之间的关系。结果表明,通过漂移系数实现的复杂动态在小型数据集中非常重要,而在大型数据集中,较简单的模型往往表现更好。为了将两者的优点结合起来,新颖的收缩先验有助于减轻维度诅咒,从而为所有考虑的情况提供有竞争力的预测。此外,我们还讨论了动态模型选择,以改进每个时间点表现最佳的单个模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian vector autoregressions?

We 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.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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