Testing for Neglected Nonlinearity in Long-Memory Models
构建了检验时间序列中除分数积分长记忆成分外是否存在未知形式非线性的方法，基于人工神经网络近似，不限制非线性参数形式，并应用于多种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.