Prediction intervals for weighted TAR forecasts

In this contribution we evaluate the forecast accuracy of a new predictor proposed for the Self Exciting Threshold AutoRegressive (SETAR) model. In more detail, we consider a weighted mean predictor, that we call weighted SETAR predictor, whose weights are obtained from the minimization of the Mean Square Forecast Errors (MSFE). Even if the `point accuracy' of this predictor has been investigated, the study of its distribution and in particular the construction of the prediction intervals has not been faced. Starting from the evaluation that the predictor follows a nonstandard distribution, in this contribution we focus the attention on different bootstrap methods for dependent data that allow to construct prediction intervals for the weighted SETAR predictor and their coverage is properly compared through a Monte Carlo study.