What is it about?

Prediction bands for linear and nonliner time series are usually generated considering only a fixed forecasting period. In order to provide a good idea about the uncertainty of the forecast over the entire forecasting horizon, new methods are proposed for SETAR models. This class of nonlinear models is increasingly used in time series analysis and forecasting as it is useful for capturing nonlinear dynamics.

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Why is it important?

Obtaining an appropriate impression about the uncertainty linked to model forecasts is of crucial importance for the application of such model based forecasts. The complex dynamic of nonlinear models requires a more careful analysis of the uncertainty of model forecasts to avoid wrong decisions.

Perspectives

The joint work on this article with the co-authors was quite inspiring. As a follow up we decided to consider the bootstrap method for generating candidate paths in more detail.

Peter Winker
Justus Liebig Universitat Giessen

Read the Original

This page is a summary of: Generating prediction bands for path forecasts from SETAR models, Studies in Nonlinear Dynamics & Econometrics, July 2017, De Gruyter,
DOI: 10.1515/snde-2016-0066.
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