A General Framework for Prediction in Time Series Models

Eric Beutner, Alexander Heinemann, Stephan Smeekes

Research output: Working paperProfessional

64 Downloads (Pure)


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

Publication series

SeriesarXiv e-prints

Cite this

Beutner, E., Heinemann, A., & Smeekes, S. (2019). A General Framework for Prediction in Time Series Models. arXiv.org at Cornell University Library. arXiv e-prints, No. 1902.01622