ARIMA Processes With ARIMA Parameters
提出一类参数服从向量ARIMAx模型的非线性非平稳时间序列模型，并基于扩展卡尔曼滤波给出识别与估计算法，用经济数据实例比较了不同估计准则。
This article introduces a general class of nonlinear and nonstationary time series models whose basic scheme is an autoregressive integrated moving average (ARIMA). The main feature is that the parameters are assumed to behave like a vector ARIMAx model in which the exogenous (x) component is represented by the regressors of the observable process. For this class a general algorithm of identification-estimation is outlined, based on the sampling information alone. The initial estimation, in particular, consists of an iterative procedure of nonlinear regressions on recursive parameter estimates generated with the extended Kalman filter. An empirical example on real economic data illustrates the method and compares alternative criteria of estimation.