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A General Framework for Prediction in Time Series Models

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Abstract

In this paper we propose a general framework to analyze prediction in time series models and show how a wide class of popular time series models satisfies this framework. We postulate a set of high-level assumptions, and formally verify these assumptions for the aforementioned time series models. Our framework coincides with that of Beutner et al. (2019, arXiv:1710.00643) who establish the validity of conditional confidence intervals for predictions made in this framework. The current paper therefore complements the results in Beutner et al. (2019, arXiv:1710.00643) by providing practically relevant applications of their theory.

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Original languageEnglish
PublisherarXiv.org at Cornell University Library
StatePublished - 5 Feb 2019

Publication series

NamearXiv e-prints
No.1902.01622