A General Framework for Prediction in Time Series Models
Econometrics
2019-02-06 v1
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.
Cite
@article{arxiv.1902.01622,
title = {A General Framework for Prediction in Time Series Models},
author = {Eric Beutner and Alexander Heinemann and Stephan Smeekes},
journal= {arXiv preprint arXiv:1902.01622},
year = {2019}
}