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Improved bootstrap prediction intervals for autoregressions. (English) Zbl 0813.62084

Summary: We consider bootstrap construction and calibration of prediction intervals for non-Gaussian autoregressions. In particular, we address the question of prediction conditioned on the last \(p\) observations of the process, for which we offer an exact simulation technique and an approximate bootstrap approach. In simulations for a variety of first- order autoregressions, we compare various nonparametric prediction intervals and find that calibration gives reasonably narrow prediction intervals with the lowest coverage probability mean squared error among the methods used.

MSC:

62M20 Inference from stochastic processes and prediction
62G09 Nonparametric statistical resampling methods
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