论文标题
均值参数随机波动率的动态收缩率在平均模型中
Dynamic shrinkage in time-varying parameter stochastic volatility in mean models
论文作者
论文摘要
成功的预测模型在简约和灵活性之间取得了平衡。这通常是通过使用合适的收缩先验来实现的，该先验会惩罚模型复杂性但也奖励模型拟合。在本说明中，我们通过引入最新的收缩技术来修改Chan（2017）中提出的平均值（SVM）模型的随机波动率，从而可以在收缩程度上进行时间变化。使用实时通货膨胀预测，我们表明，在几个关键参数上采用更灵活的先前分布，略微改善了美国，英国（英国）和欧元区（EA）的预测性能。比较样本中的结果表明，我们提出的模型在定性上产生的见解与模型的原始版本相似。
Successful forecasting models strike a balance between parsimony and flexibility. This is often achieved by employing suitable shrinkage priors that penalize model complexity but also reward model fit. In this note, we modify the stochastic volatility in mean (SVM) model proposed in Chan (2017) by introducing state-of-the-art shrinkage techniques that allow for time-variation in the degree of shrinkage. Using a real-time inflation forecast exercise, we show that employing more flexible prior distributions on several key parameters slightly improves forecast performance for the United States (US), the United Kingdom (UK) and the Euro Area (EA). Comparing in-sample results reveals that our proposed model yields qualitatively similar insights to the original version of the model.
