A General Framework for Prediction in Time Series Models

Eric Beutner; Alexander Heinemann; Stephan Smeekes

doi:10.48550/arXiv.1902.01622

A General Framework for Prediction in Time Series Models

Eric Beutner, Alexander Heinemann, Stephan Smeekes

Research output: Working paper / Preprint › Working paper

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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 language	English
Publisher	Cornell University - arXiv
DOIs	https://doi.org/10.48550/arXiv.1902.01622
Publication status	Published - 5 Feb 2019

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10.48550/arXiv.1902.01622

1902.01622v1Submitted manuscript, 339 KB

Cite this

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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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