A robust Cusum test for SETAR-type nonlinearity in time series

As a part of an effective self-exciting threshold autoregressive (SETAR) modeling methodology, it is important to identify processes exhibiting SETAR-type nonlinearity. A number of tests of nonlinearity have been developed in the literature. However, it has recently been shown that all these tests perform poorly for SETAR-type nonlinearity detection in the presence of additive outliers. In this paper, we develop an improved test for SETAR-type nonlinearity in time series. The test is an outlier-robust test based on the cumulative sums of ordered weighted residuals from generalized maximum likelihood fits. A Monte Carlo study confirms that the proposed test is competitive with existing tests for data from uncontaminated SETAR models and superior to them for SETAR data contaminated with additive outliers. Copyright © 2008 John Wiley & Sons, Ltd.