复杂而小 vs 简单而大：在贝叶斯VAR中引入漂移系数何时值得？
Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs?
AI总结：
本文通过欧元区、英国和美国的预测实验，评估时变参数VAR中模型规模与复杂性的权衡，提出收缩先验缓解维度灾难，并采用动态模型选择提升各时点预测性能。
摘要：
我们通过针对欧元区、英国和美国的详尽预测练习，评估了时变参数VAR框架中模型规模与复杂性之间的关系。结果表明，在小规模数据集中，通过漂移系数实现的复杂动态机制很重要，而在大规模数据集中，较简单的模型往往表现更好。为了结合两者的优势，新颖的收缩先验有助于缓解维度灾难，从而在所有考虑的情景中产生具有竞争力的预测。此外，我们讨论了动态模型选择，以在每个时间点上改进表现最佳的单个模型。
英文摘要：
We assess the relationship between model size and complexity in the time-varying parameter 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
