Testing for Smooth Transition Nonlinearity in the Presence of Outliers
针对平滑转换自回归模型提出异常值稳健的非线性检验方法，通过蒙特卡洛模拟和工业生产指数实例证明，该检验在存在异常值时比标准检验具有更好的水平和功效表现。
Regime-switching models, like the smooth transition autoregressive (STAR) model are typically applied to time series of moderate length. Hence, the nonlinear features which these models intend to describe may be reflected in only a few observations. \nConversely, neglected outliers in a linear time series of moderate length may incorrectly suggest STAR type nonlinearity. In this paper 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 indices illustrates that apparent nonlinearity in time series sometimes seems due to only a small number of outliers.