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Forecasting and combining competing models of exchange rate determination

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  • Carlo Altavilla
  • Paul De Grauwe

Abstract

This article investigates the out-of-sample forecast performance of a set of competing models of exchange rate determination. We compare standard linear models with models that characterize the relationship between exchange rate and the underlying fundamentals by nonlinear dynamics. Linear models tend to outperform at short forecast horizons especially when deviations from long-term equilibrium are small. In contrast, nonlinear models with more elaborate mean-reverting components dominate at longer horizons especially when deviations from long-term equilibrium are large. The results also suggest that combining different forecasting procedures generally produces more accurate forecasts than can be attained from a single model.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Applied Economics.

Volume (Year): 42 (2010)
Issue (Month): 27 ()
Pages: 3455-3480

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Handle: RePEc:taf:applec:v:42:y:2010:i:27:p:3455-3480

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Cited by:
  1. Wei, Xiaoqiao & Yang, Yuhong, 2012. "Robust forecast combinations," Journal of Econometrics, Elsevier, vol. 166(2), pages 224-236.
  2. Heijdra, Ben J. & Ligthart, Jenny E., 2007. "Fiscal policy, monopolistic competition, and finite lives," Journal of Economic Dynamics and Control, Elsevier, vol. 31(1), pages 325-359, January.
  3. Michał Rubaszek & Paweł Skrzypczyński & Grzegorz Koloch, 2010. "Forecasting the Polish Zloty with Non-Linear Models," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 2(2), pages 151-167, March.
  4. Kei Kawakami, 2013. "Conditiona l Forecast Selection from Many Forecasts: An Application to the Yen/Dollar Exchange Rate," Department of Economics - Working Papers Series 1167, The University of Melbourne.
  5. Bergmeir, Christoph & Costantini, Mauro & Benítez, José M., 2014. "On the usefulness of cross-validation for directional forecast evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 132-143.
  6. Zhang, Xinyu & Lu, Zudi & Zou, Guohua, 2013. "Adaptively combined forecasting for discrete response time series," Journal of Econometrics, Elsevier, vol. 176(1), pages 80-91.

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