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

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Author Info
Michael P. Clements (University of Warwick)
Philip Hans Franses (Erasmus University Rotterdam.)
Norman R. Swanson () (Rutgers University)

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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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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
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Handle: RePEc:rut:rutres:200309

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Keywords: Nonlinear;

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C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods

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Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Aristizábal, María Clara, 2006. "Evaluación asimétrica de una red neuronal: aplicación al caso de la inflación en Colombia," Lecturas de Economia, UNIVERSIDAD DE ANTIOQUIA - CIE. [Downloadable!]
  2. María Clara Aristizábal Restrepo, . "Evaluación asimétrica de una red neuronal artificial:Aplicación al caso de la inflación en Colombia," Borradores de Economia 377, Banco de la Republica de Colombia. [Downloadable!]
  3. María Clara Aristizábal Restrepo, 2006. "Evaluación asimétrica de una red neuronal: aplicación al caso de la inflación en Colombia," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 65, pages 73-116, Julio-Dic. [Downloadable!]
  4. Teräsvirta, Timo, 2005. "Forecasting economic variables with nonlinear models," Working Paper Series in Economics and Finance 598, Stockholm School of Economics, revised 29 Dec 2005. [Downloadable!]
    Other versions:
  5. Costanza Torricelli & Marianna Brunetti, 2006. "Economic activity and Recession Probabilities: spread predictive power in Italy," Computing in Economics and Finance 2006 350, Society for Computational Economics.
  6. Massimo Guidolin & Stuart Hyde & David McMillan & Sadayuki Ono, 2009. "Non-linear predictability in stock and bond returns: when and where is it exploitable?," Working Papers 2008-010, Federal Reserve Bank of St. Louis. [Downloadable!]
    Other versions:
  7. Nikolay Robinzonov & Klaus Wohlrabe, 2008. "Freedom of Choice in Macroeconomic Forecasting: An Illustration with German Industrial Production and Linear Models," Ifo Working Paper Series Ifo Working Paper No. 57, Ifo Institute for Economic Research at the University of Munich. [Downloadable!]
  8. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008. [Downloadable!]
  9. Marie Bessec & Othman Bouabdallah, 2005. "What causes the forecasting failure of Markov-Switching models? A Monte Carlo study," Econometrics 0503018, EconWPA. [Downloadable!]
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