Tools for non-linear time series forecasting in economics - an empirical comparison of regime switching vector autoregressive models and recurrent neural networks

BINNER, J.M., ELGER, T., NILSSON, B. and TEPPER, J.A., 2004. Tools for non-linear time series forecasting in economics - an empirical comparison of regime switching vector autoregressive models and recurrent neural networks. Advances in Economics: Applications of Artificial Intelligence in Finance and Economics (19), pp. 71-91. ISSN 0731-9053

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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: Journal article
Publication Title: Advances in Economics: Applications of Artificial Intelligence in Finance and Economics
Creators: Binner, J.M., Elger, T., Nilsson, B. and Tepper, J.A.
Publisher: Emerald
Date: 2004
Number: 19
ISSN: 0731-9053
Identifiers:
NumberType
10.1016/S0731-9053(04)19003-8DOI
Rights: © 2004 Emerald Group Publishing Limited.
Divisions: Schools > School of Science and Technology
Depositing User: EPrints Services
Date Added: 09 Oct 2015 10:31
Last Modified: 23 Aug 2016 09:10
URI: http://irep.ntu.ac.uk/id/eprint/14282

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