论文标题
稀疏的动态因子模型通过变异推理带有加载选择
Sparse Dynamic Factor Models with Loading Selection by Variational Inference
论文作者
论文摘要
在本文中，我们开发了一种新的方法，用于使用变分推断估算大型且稀疏的动态因子模型，也允许缺少数据。受贝叶斯变量选择的启发，我们将平板和尖峰先验应用于因子负载上以应对稀疏性。开发了一种算法，以查找后验分布的本地最佳平均场近似值，该算法可以快速地获得计算，从而适合于现实播放和实践中经常更新的分析。我们在两个仿真实验中评估了该方法，该方法显示出良好的稀疏模式以及精确的负载和因子估计。
In this paper we develop a novel approach for estimating large and sparse dynamic factor models using variational inference, also allowing for missing data. Inspired by Bayesian variable selection, we apply slab-and-spike priors onto the factor loadings to deal with sparsity. An algorithm is developed to find locally optimal mean field approximations of posterior distributions, which can be obtained computationally fast, making it suitable for nowcasting and frequently updated analyses in practice. We evaluate the method in two simulation experiments, which show well identified sparsity patterns and precise loading and factor estimation.
