Improved multivariate prediction regions for Markov process models

Paolo Vidoni ()
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Paolo Vidoni: University of Udine

Statistical Methods & Applications, 2017, vol. 26, issue 1, No 1, 18 pages

Abstract: Abstract This paper concerns the specification of multivariate prediction regions which may be useful in time series applications whenever we aim at considering not just one single forecast but a group of consecutive forecasts. We review a general result on improved multivariate prediction and we use it in order to calculate conditional prediction intervals for Markov process models so that the associated coverage probability turns out to be close to the target value. This improved solution is asymptotically superior to the estimative one, which is simpler but it may lead to unreliable predictive conclusions. An application to general autoregressive models is presented, focusing in particular on AR and ARCH models.

Keywords: Autoregressive model; Coverage probability; Estimative prediction limits; Simultaneous prediction limits; Time series (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10260-016-0362-y

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