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Forecasting economic and financial time-series with non-linear models

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  • Clements, Michael P.
  • Franses, Philip Hans
  • Swanson, Norman R.

Abstract

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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Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 20 (2004)
Issue (Month): 2 ()
Pages: 169-183

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Handle: RePEc:eee:intfor:v:20:y:2004:i:2:p:169-183

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