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 i s that the parameters are assumed to behave like a vector ARIMAx model in which the exogenous (x) component is represented by the regressors o f 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 o f an iterative procedure of nonlinear regressions on recursive paramet er estimates generated with the extended Kalman filter.
