A Monte Carlo Comparison of Time Varying Parameter and Multiprocess Mixture Models in the Presence of Structural Shifts and Outliers
这篇论文用蒙特卡洛模拟比较了多过程混合模型和随机游走时变参数模型在数据有结构突变和异常值时的表现，发现混合模型更好，而传统模型表现差。对研究经济变量结构变化的学者有参考价值。
This Monte Carlo study compares the performance of a recently proposed multiprocess mixture model and a more traditional random walk time-varying parameter model in the face of structural shifts and outliers. The mixture model performs well and the latter model performs poorly. This finding is of general interest since investigators often adopt random-walk time-varying parameter models to accommodate potential regime shifts in regression relationships. The findings suggest that the time-varying parameter estimation procedure is unlikely to find abrupt shifts, since the time-varying parameter estimates are contaminated by the outliers and regime shifts. Copyright 1993 by MIT Press.