IDENTIFICATION OF NONLINEAR TIME-SERIES - 1ST ORDER CHARACTERIZATION AND ORDER DETERMINATION
IDENTIFICATION OF NONLINEAR TIME-SERIES - 1ST ORDER CHARACTERIZATION AND ORDER DETERMINATION
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DOI：
10.1093/biomet/77.4.669
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发表时间：
1990-12-01
期刊：
影响因子：
2.7
通讯作者：
TJOSTHEIM, D
中科院分区：
文献类型：
--
作者：
AUESTAD, B;TJOSTHEIM, D
我们研究了使用条件均值和条件方差的非参数估计来识别非线性时间序列的可能性。它表明，大多数非线性模型满足应用非参数渐近理论所需的假设。抽样变化的条件量进行了研究模拟和解释的渐近参数的一阶非线性自回归过程。条件均值和方差可用于识别目的，但必须注意偏倚和误指定效应。我们还提出了一个判定一般非线性模型阶数的准则。该标准在一定程度上被启发式方法证明是合理的，但从有限的一组模拟实验中获得了令人鼓舞的结果。几个开放的问题被确定和说明。
We study the possibility of identifying nonlinear time series using nonparametric estimates of the conditional mean and conditional variance. It is shown that most nonlinear models satisfy the assumptions needed to apply nonparametric asymptotic theory. Sampling variations of the conditional quantities are studied by simulation and explained by asymptotic arguments for a number of first-order nonlinear autoregressive processes. The conditional mean and variance can be used for identification purposes, but one must be aware of bias and misspecification effects. We also propose a criterion for determining the order of a general nonlinear model. The criterion is justified in parts by heuristics, but encouraging results are obtained from a limited set of simulation experiments. Several open problems are identified and stated.