Functional-Coefficient Regression Models for Nonlinear Time Series
Functional-Coefficient Regression Models for Nonlinear Time Series
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DOI：
10.1080/01621459.2000.10474284
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发表时间：
2000-09
影响因子：
3.7
通讯作者：
Z. Cai;Jianqing Fan;Q. Yao
中科院分区：
文献类型：
--
作者：
Z. Cai;Jianqing Fan;Q. Yao
摘要将局部线性回归技术应用于时间序列数据的函数系数回归模型的估计。这些模型包括阈值自回归模型和函数系数自回归模型作为特例，但具有额外的优势，如描绘更精细的结构的基本动态和更好的后样本预测性能。提出了一种新的自举检验模型的拟合优度和带宽选择器的基础上新定义的交叉验证估计的预期预测误差。所提出的方法是数据分析和足够的灵活性来分析复杂的和多变量的非线性结构，而不会受到“维数灾难”。在α-混合条件下，研究了估计量的渐近性质.模拟和真实的数据的例子都用于说明。
Abstract The local linear regression technique is applied to estimation of functional-coefficient regression models for time series data. The models include threshold autoregressive models and functional-coefficient autoregressive models as special cases but with the added advantages such as depicting finer structure of the underlying dynamics and better postsample forecasting performance. Also proposed are a new bootstrap test for the goodness of fit of models and a bandwidth selector based on newly defined cross-validatory estimation for the expected forecasting errors. The proposed methodology is data-analytic and of sufficient flexibility to analyze complex and multivariate nonlinear structures without suffering from the “curse of dimensionality.” The asymptotic properties of the proposed estimators are investigated under the α-mixing condition. Both simulated and real data examples are used for illustration.