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

Listed author(s):
  • Michael P. Clements

    (University of Warwick)

  • Philip Hans Franses

    (Erasmus University Rotterdam.)

  • Norman R. Swanson

    ()

    (Rutgers University)

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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Paper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 200309.

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Date of creation: 13 Oct 2003
Handle: RePEc:rut:rutres:200309
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