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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. Michal Rubaszek & Pawel Skrzypczynski & Grzegorz Koloch, 2011. "Forecasting the Polish zloty with non-linear models," National Bank of Poland Working Papers 81, National Bank of Poland, Economic Institute.
  2. Heijdra, B.J. & Ligthart, J.E., 2006. "Fiscal Policy, Monopolistic Competition and Finite Lives," Open Access publications from Tilburg University urn:nbn:nl:ui:12-199395, Tilburg University.
  3. 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.
  4. 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.
  5. Wei, Xiaoqiao & Yang, Yuhong, 2012. "Robust forecast combinations," Journal of Econometrics, Elsevier, vol. 166(2), pages 224-236.
  6. 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.

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