A New Recursive Estimation Method for Single Input Single Output Models
提出一种新的递归估计方法用于动态时间序列模型，避免更新海森矩阵，利用Fisher信息矩阵更新估计量，在弱假设下证明了估计量的一致性和渐近正态性，蒙特卡洛实验显示收敛性良好。
This article is devoted to a new recursive estimation method for dynamic time series models, more precisely for single input single output models. In that method, the recurrence for updating the Hessian is avoided, but the recurrence for updating the estimator makes use of the Fisher information matrix. The asymptotic properties, consistency and asymptotic normality, of the new estimator are obtained under weak assumptions. Monte Carlo experiments and examples indicate that the estimates converge well, comparatively with alternative methods.