We evaluate the forecast accuracy of a new predictor proposed for the Self Exciting Threshold AutoRegressive (SETAR) model. In particular we focus the attention on a new predictor obtained as weighted mean of the past observations, whose weights are obtained from the minimization of the Mean Square Forecast Errors. Even if the “point accuracy” of this weighted predictor has been performed, the study of its distribution and in particular the construction of the prediction intervals has not been faced. Starting from the evaluation that the prediction errors, obtained from the difference between the true future values and the predicted values, follow 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.
Bootstrap prediction intervals for weighted TAR predictors
francesco giordano;marcella niglio
2019
Abstract
We evaluate the forecast accuracy of a new predictor proposed for the Self Exciting Threshold AutoRegressive (SETAR) model. In particular we focus the attention on a new predictor obtained as weighted mean of the past observations, whose weights are obtained from the minimization of the Mean Square Forecast Errors. Even if the “point accuracy” of this weighted predictor has been performed, the study of its distribution and in particular the construction of the prediction intervals has not been faced. Starting from the evaluation that the prediction errors, obtained from the difference between the true future values and the predicted values, follow 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
