A General Framework for Prediction in Time Series Models

• Tác giả: Eric Beutner, Alexander Heinemann, Stephan Smeekes
• Ngôn ngữ: eng
• Ký hiệu phân loại: 338.544 General production forecasting and forecasts
• Thông tin xuất bản: 2019
• Bộ sưu tập: Metadata
• ID: 162604

Tóm tắt

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.