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Binner, J.M., Elger, T., Nilsson, B. and Tepper, J.A. 
ORCID: 0000-0001-7339-0132,
2004.
Tools for non-linear time series forecasting in economics - an empirical comparison of regime switching vector autoregressive models and recurrent neural networks.
In: J.M. Binner, G. Kendall and S.-H. Chen, eds.,
Applications of artificial intelligence in finance and economics.
Advances in econometrics
(19).
Leeds: Emerald, pp. 71-91.
ISBN 9780762311507
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Abstract
The purpose of this study is to contrast the forecasting performance of two non-linear models, a regime-switching vector autoregressive model (RS-VAR) and a recurrent neu-ral network (RNN), to that of a linear benchmark VAR model. Our specific forecasting experiment is UK inflation and we utilize monthly data from 1969-2003. The RS-VAR and the RNN perform approximately on par over both monthly and annual forecast hori-zons. Both non-linear models perform significantly better than the VAR model. Keywords: Inflation forecasting, regime-switching vector autoregressive model, recurrent neural network.
| Item Type: | Chapter in book | ||||
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| Creators: | Binner, J.M., Elger, T., Nilsson, B. and Tepper, J.A. | ||||
| Publisher: | Emerald | ||||
| Place of Publication: | Leeds | ||||
| Date: | 2004 | ||||
| Number: | 19 | ||||
| ISBN: | 9780762311507 | ||||
| ISSN: | 0731-9053 | ||||
| Identifiers: |
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| Rights: | © 2004 Emerald Group Publishing Limited. | ||||
| Divisions: | Schools > School of Science and Technology | ||||
| Record created by: | EPrints Services | ||||
| Date Added: | 09 Oct 2015 11:00 | ||||
| Last Modified: | 08 Feb 2024 11:40 | ||||
| URI: | https://irep.ntu.ac.uk/id/eprint/21476 |
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