In this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among non-linear forecasting models for economic and financial time series. We review theoretical and empirical issues, including predictive density, interval and point evaluation and model selection, loss functions, data-mining, and aggregation. In addition, we argue that although the evidence in favor of constructing forecasts using non-linear models is rather sparse, there is reason to be optimistic. However, much remains to be done. Finally, we outline a variety of topics for future research, and discuss a number of areas which have received considerable attention in the recent literature, but where many questions remain.

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International Journal of Forecasting
Erasmus Research Institute of Management

Franses, Ph.H.B.F, Clements, M.P, & Swanson, N. (2004). Forecasting economic and financial time series with non-linear models. International Journal of Forecasting (Vol. 20, pp. 169–183). doi:10.1016/j.ijforecast.2003.10.004