Forecasting cointegrated nonstationary time series with time-varying variance
Forecasting cointegrated nonstationary time series with time-varying variance
复制标题
预测具有时变方差的协整非平稳时间序列
DOI：
10.1016/j.jeconom.2016.09.012
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发表时间：
2017
影响因子：
6.3
通讯作者：
Yi Yanping
中科院分区：
文献类型：
--
作者：
Tu Yundong;Yi Yanping
在协整向量自回归（VAR）模型中，误差修正项往往对预测有不确定的影响，因此我们关注协整关系在预测中的包含或排除问题。本文研究了具有异质性方差或方差断裂的协整VAR模型的模型平均策略。利用估计的协整秩和其他数据信息来制定模型的平均权重。这种特定但未知的时变方差模式对模型权重的选择具有重要影响。我们的数值结果强烈支持Mallows平均估计器，但对常用的预测试方法持谨慎态度。
In cointegrated vector autoregressive (VAR) models, error correction terms often have indeterminate effects on forecasting, thus we are concerned with inclusion or exclusion of the cointegration relation in forecast. This paper considers the model averaging strategies for cointegrated VAR models with heterogeneous variance or variance breaks. The estimated cointegration rank along with other data information are used to formulate the model averaging weights. This specific but unknown pattern of time-varying variances has nontrivial effects on the choices of model weights. Our numerical results strongly advocate the Mallows averaging estimator, but caution against the commonly used pre-testing approach.