Tools for Non-linear Time Series Forecasting: An Empirical Comparison of Regime Switching Autoregressive Models and Recurrent Neural Networks

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Published in Advances in Econometrics, 2004, vol. 19, 71-92.

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 UK 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.

Keywords: inflation forecasting, regime-switching vector autoregressive model, recurrent neural network.