A two–stage approach to additive time series models
A two–stage approach to additive time series models
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DOI：
10.1111/1467-9574.00210
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发表时间：
2002-11
影响因子：
1.5
通讯作者：
Z. Cai
中科院分区：
文献类型：
--
作者：
Z. Cai
对于非线性加性时间序列模型，文献中用于估计非参数加性分量的一种有吸引力的方法是投影方法。在本文中，证明了投影方法在渐近意义上可能不是有效的。为了有效地估计加性分量，提出了一种两阶段方法以及局部线性拟合和基于非参数版本的 Akaike 信息准则的新带宽选择器。结果表明，两阶段方法不仅提高了效率，而且使带宽选择相对容易。此外，还建立了所得估计量的渐近正态性。进行了一项小型模拟研究来说明所提出的方法，并将两阶段方法应用于计量经济学的实际例子。
For nonlinear additive time series models, an appealing approach used in the literature to estimate the nonparametric additive components is the projection method. In this paper, it is demonstrated that the projection method might not be efficient in an asymptotic sense. To estimate additive components efficiently, a two–stage approach is proposed together with a local linear fitting and a new bandwidth selector based on the nonparametric version of the Akaike information criterion. It is shown that the two–stage method not only achieves efficiency but also makes bandwidth selection relatively easier. Also, the asymptotic normality of the resulting estimator is established. A small simulation study is carried out to illustrate the proposed methodology and the two–stage approach is applied to a real example from econometrics.