Forecasting economic and financial time series with non-linear models
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.
|Keywords||economics, finance, financial economics, non-linear models, time series|
|Persistent URL||dx.doi.org/10.1016/j.ijforecast.2003.10.004, hdl.handle.net/1765/2169|
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, 20(2), 169–183. doi:10.1016/j.ijforecast.2003.10.004