论文标题
Farima模型的快速校准
Fast calibration of weak FARIMA models
论文作者
论文摘要
在本文中，我们研究了LE CAM的一步估计器的渐近性能，用于弱分数自动回归整合运动平均（FARIMA）模型。对于这些模型，噪声是不相关的，但既不一定是独立的也不是Martingale差异。我们在一些规律性的假设下表明，单步估计量与最小二乘估计器相同的渐近方差非常一致，渐近正常。我们通过模拟显示，与最小二乘估计器相比，提出的估计器会缩短计算时间。提出了用于提供时间序列的远程计算指标的应用程序。
In this paper, we investigate the asymptotic properties of Le Cam's one-step estimator for weak Fractionally AutoRegressive Integrated Moving-Average (FARIMA) models. For these models, noises are uncorrelated but neither necessarily independent nor martingale differences errors. We show under some regularity assumptions that the one-step estimator is strongly consistent and asymptotically normal with the same asymptotic variance as the least squares estimator. We show through simulations that the proposed estimator reduces computational time compared with the least squares estimator. An application for providing remotely computed indicators for time series is proposed.
