February 5th, 2019 by Stephan Smeekes
A General Framework for Prediction in Time Series Models
Eric Beutner, Alexander Heinemann and Stephan Smeekes
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) 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) by providing practically relevant applications of their theory.