Bayesian analysis of static and dynamic factor models: An ex-post approach towards the rotation problem
Bayesian analysis of static and dynamic factor models: An ex-post approach towards the rotation problem
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DOI：
10.1016/j.jeconom.2015.10.010
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发表时间：
2016-05-01
影响因子：
6.3
通讯作者：
Pape, Markus
中科院分区：
文献类型：
--
作者：
Assmann, Christian;Boysen-Hogrefe, Jens;Pape, Markus
由于其不确定性，静态和动态因子模型需要确定假设以保证参数估计器的唯一性。参数估计器相对于正交变换的不确定性称为旋转问题。贝叶斯因子分析中解决旋转问题的典型策略是通过简并和截断的先验分布对某些模型参数引入事前约束。然而，这种策略会产生后验分布，其形状取决于数据集中变量的排序。我们提出了一种替代方法，使用 Procrustean 后处理事后解决旋转问题。使用包含 120 个宏观经济时间序列的已建立数据集进行模拟研究和实证应用，说明了后验估计器的阶次不变性。揭示了事后方法在收敛性、统计和数值准确性方面的有利特性。 (C) 2015 Elsevier B.V. 保留所有权利。
Due to their indeterminacies, static and dynamic factor models require identifying assumptions to guarantee uniqueness of the parameter estimator. The indeterminacy of the parameter estimator with respect to an orthogonal transformation is known as the rotation problem. The typical strategy in Bayesian factor analysis to solve the rotation problem is to introduce ex-ante constraints on certain model parameters via degenerate and truncated prior distributions. This strategy, however, results in posterior distributions whose shapes depend on the ordering of the variables in the data set. We propose an alternative approach where the rotation problem is solved ex-post using Procrustean postprocessing. The resulting order invariance of the posterior estimator is illustrated in a simulation study and an empirical application using an established data set containing 120 macroeconomic time series. Favorable properties of the ex-post approach with respect to convergence, statistical and numerical accuracy are revealed. (C) 2015 Elsevier B.V. All rights reserved.