Functional-coefficient regression models for nonlinear time series
Functional-coefficient regression models for nonlinear time series
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DOI：
10.2307/2669476
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发表时间：
2000-09-01
影响因子：
3.7
通讯作者：
Yao, QW
中科院分区：
文献类型：
--
作者：
Cai, ZW;Fan, JQ;Yao, QW
局部线性回归技术被应用于时间序列数据的函数系数回归模型的估计。这些模型包括门限自回归模型和函数系数自回归模型作为特殊情况，但具有一些额外的优势，比如描绘潜在动态的更精细结构以及更好的样本外预测性能。还提出了一种用于模型拟合优度的新自助法检验，以及一种基于新定义的预期预测误差交叉验证估计的带宽选择器。所提出的方法是数据分析性的，具有足够的灵活性来分析复杂的和多元的非线性结构，而不会受到“维数灾难”的影响。在α -混合条件下研究了所提出的估计量的渐近性质。通过模拟数据和实际数据的例子进行了说明。
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 or-mixing condition. Both simulated and real data examples are used for illustration.