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Tools For Non-Linear Time Series Forecasting In Economics – An Empirical Comparison Of Regime Switching Vector Autoregressive Models And Recurrent Neural Networks

In: Applications of Artificial Intelligence in Finance and Economics

Author

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  • Jane M. Binner
  • Thomas Elger
  • Birger Nilsson
  • Jonathan A. Tepper

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 neural network (RNN), to that of a linear benchmark VAR model. Our specific forecasting experiment is U.K. inflation and we utilize monthly data from 1969 to 2003. The RS-VAR and the RNN perform approximately on par over both monthly and annual forecast horizons. Both non-linear models perform significantly better than the VAR model.

Suggested Citation

  • Jane M. Binner & Thomas Elger & Birger Nilsson & Jonathan A. Tepper, 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 Econometrics, in: Applications of Artificial Intelligence in Finance and Economics, pages 71-91, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-9053(04)19003-8
    DOI: 10.1016/S0731-9053(04)19003-8
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