论文标题
具有不完整数据的动态因子模型的后验估计
Maximum a Posteriori Estimation of Dynamic Factor Models with Incomplete Data
论文作者
论文摘要
在本文中，我们提出了一种最大程度的后验估计的方法，该参数在动态因子模型中具有不完整的数据。我们将Bańbura＆Modugno（2014）（2014年）的最大可能性期望最大化迭代扩展到惩罚对应物，通过以明尼苏达州的先验方式应用参数收缩，还考虑了动态上加载到变量上的因素。考虑了一种启发式和适应的收缩计划。该算法适用于任何丢失数据的任意模式，包括不同的出版物日期，样本长度和频率。该方法在一项蒙特卡洛研究中进行评估，通常表现良好，至少与最大似然相当。
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 Bańbura & 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.
