Overcoming Nonadmissibility in ARIMA-Model-Based Signal Extraction
分析了基于ARIMA模型的时间序列分解出现非容许性的情况，提出使用头重模型解决该问题，并通过实例和实证应用展示了改进效果。
We analyze the situation in which the decomposition of a time series into orthogonal balanced components as performed by the AR IMA-model-based (AMB) method is nonadmissible. We show that considering top-heavy models for the components can solve the problem. The top-heavy decomposition is derived and the improvement achieved is illustrated by an application to a class of models often encountered in practice. Two empirical applications allow us to draw a comparison with the results yielded by the AMB decomposition of an approximated model by using an ad hoc filter such as X11-ARIMA and by direct specification of the structural time series models.