Subsampling inference in threshold autoregressive models

Abstract

This paper discusses inference in self-exciting threshold autoregressive (SETAR) models. Of main interest is inference for the threshold parameter. It is well-known that the asymptotics of the corresponding estimator depend upon whether the SETAR model is continuous or not. In the continuous case, the limiting distribution is normal and standard inference is possible. In the discontinuous case, the limiting distribution is non-normal and it is not known how to estimate it consistently. We show that valid inference can be drawn by the use of the subsampling method. Moreover, the method can even be extended to situations where the (dis)continuity of the model is unknown. In this case, the inference for the regression parameters of the model also becomes difficult and subsampling can be used again. In addition, we consider an hypothesis test for the continuity of a SETAR model. A simulation study examines small sample performance and an application illustrates how the proposed methodology works in practice.