A note on simultaneous calibrated prediction intervals for time series

Giovanni Fonseca (), Federica Giummolè () and Paolo Vidoni ()
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Giovanni Fonseca: Università di Udine
Federica Giummolè: Università Ca’ Foscari Venezia
Paolo Vidoni: Università di Udine

Statistical Methods & Applications, 2021, vol. 30, issue 1, No 13, 317-330

Abstract: Abstract This paper deals with simultaneous prediction for time series models. In particular, it presents a simple procedure which gives well-calibrated simultaneous prediction intervals with coverage probability close to the target nominal value. Although the exact computation of the proposed intervals is usually not feasible, an approximation can be easily attained by means of a suitable bootstrap simulation procedure. This new predictive solution is much simpler to compute than those ones already proposed in the literature, based on asymptotic calculations. Applications of the bootstrap calibrated procedure to AR, MA and ARCH models are presented.

Keywords: Bootstrap; Coverage probability; Monte Carlo simulation; Prediction region; Simultaneous prediction intervals; Time series (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10260-020-00526-6

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