TOOLS FOR NON-LINEAR TIME SERIES FORECASTING IN ECONOMICS – AN EMPIRICAL COMPARISON OF REGIME SWITCHING VECTOR AUTOREGRESSIVE MODELS AND RECURRENT NEURAL NETWORKS

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.

Citation

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", Binner, J.M., Kendall, G. and Chen, S.-H. (Ed.) Applications of Artificial Intelligence in Finance and Economics (Advances in Econometrics, Vol. 19), Emerald Group Publishing Limited, Leeds, pp. 71-91. https://doi.org/10.1016/S0731-9053(04)19003-8