A General Framework for Prediction in Time Series Models

Research output: Working paperProfessional

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

Cite this

Beutner, E., Heinemann, A., & Smeekes, S. (2019). A General Framework for Prediction in Time Series Models. arXiv.org at Cornell University Library.
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title = "A General Framework for Prediction in Time Series Models",
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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Beutner, E, Heinemann, A & Smeekes, S 2019 'A General Framework for Prediction in Time Series Models' arXiv.org at Cornell University Library.

A General Framework for Prediction in Time Series Models. / Beutner, Eric; Heinemann, Alexander; Smeekes, Stephan.

arXiv.org at Cornell University Library, 2019.

Research output: Working paperProfessional

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AB - 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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Beutner E, Heinemann A, Smeekes S. A General Framework for Prediction in Time Series Models. arXiv.org at Cornell University Library. 2019 Feb 5.