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.

翻译：在本文中,我们开发了一种新颖的方法,用变异的推论来估计大型和稀疏的动态要素模型,同时也考虑到缺失的数据。在贝叶西亚变量选择的启发下,我们在元素负荷上应用了板块和擦拭前缀来应对聚变。我们开发了一种算法,以找到本地最佳的后星分布平均场近似值,这种近似值可以快速计算获得,使之适合现在的预报和经常更新的实际分析。我们在两个模拟实验中评估了这种方法,这些实验显示了明确确定的聚变模式以及精确的负荷和要素估计。