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

Michael Clements, Philip Hans Franses and Norman Swanson ()

Departmental Working Papers from Rutgers University, Department of Economics

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.

Keywords: Nonlinear (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fin
Date: 2003-10-13
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http://www.sas.rutgers.edu/virtual/snde/wp/2003-09.pdf (application/pdf)

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Journal Article: Forecasting economic and financial time-series with non-linear models (2004) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:rut:rutres:200309

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