@techreport{d8c3534ea7c54255988b27d145b00000,
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. ",
author = "Eric Beutner and Alexander Heinemann and Stephan Smeekes",
year = "2019",
month = feb,
day = "5",
language = "English",
series = "arXiv e-prints",
number = "1902.01622",
publisher = "arXiv.org at Cornell University Library",
type = "WorkingPaper",
institution = "arXiv.org at Cornell University Library",
}