具有平滑迁移的ARFIMA模型及其应用

Smooth Transition Autoregressive Fractional Moving Average Model and Empirical Analysis:A Case of China's Inflation Rate

本文基于描述长记忆性的ARFIMA模型和具有结构性转变的平滑迁移模型,提出了联合检验两种时间序列性质的STARFIMA模型,并给出了估计模型系数的估计方法和检验非线性的刀切似然比方法。应用我国通货膨胀率的时间序列数据,我们应用Logistic型STARFIMA模型进行经验分析时发现,STARFIMA模型具有比ARFIMA模型更好的模拟效果和精度,而且该模型分别捕捉到了以通货膨胀率自身和加速通货膨胀率为转移变量的结构性转变,并发现在引入结构转变之后的通货膨胀率序列的记忆性变强的特征。

On the base is of ARFIMA model and smooth transition model which describe.long memory and structural change respectively, this paper proposes a STARFIMA model to jointly test these two properties of time series and presents the methods of parameter estimation and bootstrap likelihood ratio test for null of linearity. As a case of China＇s inflation rate, we find that STARFIMA model can simulate better than the linear ARFIMA model by empirical analysis with logistic smooth transition ARFIMA model. Moreover, this model can capture structural change with transition variables of inflation rate and increasing inflation rate, and the results suggest that the memory becomes strong when considering structural change and displays long range dependence.