论文标题
用贝叶斯Vars预测宏观经济数据：稀疏还是致密？这取决于！
Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends!
论文作者
论文摘要
当涉及到建模和预测宏观经济变量时，向量自动化（VAR）广泛应用。但是，在高维度中，它们容易过度拟合。贝叶斯方法（更具体地收缩先验）已证明在改善预测性能方面已成功。在本文中，我们介绍了半全球框架，在该框架中，我们用群体特定的收缩参数代替了传统的全局收缩参数。我们展示了如何将该框架应用于各种收缩先验，例如全球 - 位置先验和随机搜索变量选择先验。我们在广泛的模拟研究和美国经济的经验应用预测数据中证明了拟议框架的优点。此外，我们更多地阐明了正在进行的``稀疏幻象''辩论，发现在评估的经济变量和跨时间范围内的稀疏/密集先验下的预测性能各不相同。但是，平均动态模型可以结合两个世界的优点。
Vector autogressions (VARs) are widely applied when it comes to modeling and forecasting macroeconomic variables. In high dimensions, however, they are prone to overfitting. Bayesian methods, more concretely shrinkage priors, have shown to be successful in improving prediction performance. In the present paper, we introduce the semi-global framework, in which we replace the traditional global shrinkage parameter with group-specific shrinkage parameters. We show how this framework can be applied to various shrinkage priors, such as global-local priors and stochastic search variable selection priors. We demonstrate the virtues of the proposed framework in an extensive simulation study and in an empirical application forecasting data of the US economy. Further, we shed more light on the ongoing ``Illusion of Sparsity'' debate, finding that forecasting performances under sparse/dense priors vary across evaluated economic variables and across time frames. Dynamic model averaging, however, can combine the merits of both worlds.
