Subsampling Inference in Threshold Autoregressive Models
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 cannot be estimated consistently. We show 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, also the inference for the regression parameters of the model becomes difficult and subsampling can be used advantageously there as well. In addition, we consider an hypothesis test for the continuity of the SETAR model. A simulation study examines small sample performance.
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