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.

中文翻译：

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复杂且小型与简单且大型：在贝叶斯向量自回归中引入漂移系数何时会获得回报？

我们通过对欧元区、英国和美国进行全面的预测练习，评估时变参数向量自回归 (VAR) 框架中模型大小和复杂性之间的关系。事实证明，通过漂移系数实现的复杂动态在小数据集中很重要，而更简单的模型往往在大数据集中表现更好。为了结合两个世界的优点，新颖的收缩先验有助于减轻维数灾难，从而对所有考虑的场景进行有竞争力的预测。此外，我们讨论动态模型选择，以改进每个时间点的最佳性能单个模型。