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A General Framework for Prediction in Time Series Models

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Document pages: 47 pages

Abstract: In this paper we propose a general framework to analyze prediction in timeseries models and show how a wide class of popular time series models satisfiesthis framework. We postulate a set of high-level assumptions, and formallyverify these assumptions for the aforementioned time series models. Ourframework coincides with that of Beutner et al. (2019, arXiv:1710.00643) whoestablish the validity of conditional confidence intervals for predictions madein this framework. The current paper therefore complements the results inBeutner et al. (2019, arXiv:1710.00643) by providing practically relevantapplications of their theory.

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