A General Framework for Prediction in Time Series Models

Eric Beutner, Alexander Heinemann and Stephan Smeekes

Papers from arXiv.org

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.

Date: 2019-02
New Economics Papers: this item is included in nep-ecm and nep-ets
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