Testing for Smooth Transition Nonlinearity in the Presence of Outliers
针对中等长度时间序列中可能因异常值误判为STAR型非线性的问题，提出了稳健的检验方法，并通过模拟和工业产值数据验证其有效性。
Regime-switching models, like the smooth transition autoregressive [STAR] model, are typically applied to time series of moderate length. Hence, the nonlinear features that these models intend to describe may be reflected in only a few observations. Conversely, neglected outliers in a linear time series of moderate length may incorrectly suggest STAR (or other) type(s of) nonlinearity. In this article we propose outlier robust tests for STAR-type nonlinearity. These tests are designed such that they have a better level and power behavior than standard nonrobust tests in situations with outliers. We formally derive local and global robustness properties of the new tests. Extensive Monte Carlo simulations show the practical usefulness of the robust tests. An application to several quarterly industrial production indexes illustrates that apparent nonlinearity in time series sometimes seems due to only a few outliers.