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Advances in Data Analysis and Classification
Published in: Advances in Data Analysis and Classification 1/2015

01-03-2015 | Regular Article

Modeling and forecasting interval time series with threshold models

Authors: Paulo M. M. Rodrigues, Nazarii Salish

Published in: Advances in Data Analysis and Classification | Issue 1/2015

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Abstract

This paper proposes threshold models to analyze and forecast interval-valued time series. A relatively simple algorithm is proposed to obtain least square estimates of the threshold and slope parameters. The construction of forecasts based on the proposed model and methods for the analysis of their forecast performance are also introduced and discussed, as well as forecasting procedures based on the combination of different models. To illustrate the usefulness of the proposed methods, an empirical application on a weekly sample of S&P500 index returns is provided. The results obtained are encouraging and compare very favorably to available procedures.
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Appendix
Available only for authorised users
Footnotes
1
Note that it is not clear if the results obtained in the following sections will hold for the lower-upper bound representation of ITS, and we do not pursue this extension in this paper. However, this presents an interesting line of research for further investigation.
 
2
Following the suggestion of one referee, to compare predictive accuracy we have also conducted Diebold–Mariano tests (see Appendix) and the results obtained corroborate these conclusions.
 
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Metadata
Title
Modeling and forecasting interval time series with threshold models
Authors
Paulo M. M. Rodrigues
Nazarii Salish
Publication date
01-03-2015
Publisher
Springer Berlin Heidelberg
Published in
Advances in Data Analysis and Classification / Issue 1/2015
Print ISSN: 1862-5347
Electronic ISSN: 1862-5355
DOI
https://doi.org/10.1007/s11634-014-0170-x

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