In this paper, we present a method of maximum a posteriori estimation of parameters in dynamic factor models with incomplete data. We extend maximum likelihood expectation maximization iterations by Ba\'nbura & Modugno (2014) to penalized counterparts by applying parameter shrinkage in a Minnesota prior style fashion, also considering factors loading onto variables dynamically. A heuristic and adapting shrinkage scheme is considered. The algorithm is applicable to any arbitrary pattern of missing data, including different publication dates, sample lengths and frequencies. The method is evaluated in a Monte Carlo study, generally performing favourably, and at least comparably, to maximum likelihood.
翻译:在本文中,我们提出了一个对动态要素模型中的参数进行最大事后估计的方法,该模型的数据不完整。我们通过在明尼苏达州先前的风格中应用参数缩缩,将最大可能预期的Ba\'nbura & Modugno 和 Modugno(2014年)的最大化迭代扩大到受处罚的对应方,同时考虑动态地将各种因素加到变量中来。我们考虑了一个超常和适应性缩放计划。算法适用于任何任意的缺失数据模式,包括不同的发布日期、样本长度和频率。在蒙特卡洛的研究中评估了这种方法,通常表现良好,至少可以与最大的可能性相比。