Testing for neglected nonlinearity in long-memory models

被引：23
|
作者：
Baillie, Richard T. [1 ]
Kapetanios, George
机构：
[1] Michigan State Univ, Dept Econ & Finance, E Lansing, MI 48824 USA
[2] Queen Mary Univ London, Dept Econ, London E1 4NS, England
来源：
JOURNAL OF BUSINESS & ECONOMIC STATISTICS | 2007年 / 25卷 / 04期
关键词：
absolute return; artificial neural network; long memory; nonlinearity; real exchange; rate; realized volatility;
D O I：
10.1198/073500106000000305
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
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.
引用
收藏
页码：447 / 461
页数：15
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