Testing for Neglected Nonlinearity in Long-Memory Models
Testing for Neglected Nonlinearity in Long-Memory Models
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DOI：
10.1198/073500106000000305
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发表时间：
2007-10
影响因子：
3
通讯作者：
R. Baillie;G. Kapetanios
中科院分区：
文献类型：
--
作者：
R. Baillie;G. Kapetanios
本文构造了在时间序列过程中，除了分数积分的长记忆分量之外，还存在未知形式的非线性的检验。这些测试基于人工神经网络近似，并且不限制非线性的参数形式。得到了新测试的一些理论结果，并提供了详细的仿真证据来验证测试的威力。然后，新的方法被应用于各种各样的经济和金融时间序列。
This article constructs tests for the presence of nonlinearity of unknown form in addition to a fractionally integrated, long-memory component in a time series process. The tests are based on artificial neural network approximations and do not restrict the parametric form of the nonlinearity. Some theoretical results for the new tests are obtained, and detailed simulation evidence on the power of the tests is presented. The new methodology is then applied to a wide variety of economic and financial time series.