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Beating the random walk in Central and Eastern Europe

  • Jesús Crespo Cuaresma

    (University of Vienna, Austria)

  • Jaroslava Hlouskova

    (Institute for Advanced Studies, Austria)

We compare the accuracy of vector autoregressive (VAR), restricted vector autoregressive (RVAR), Bayesian vector autoregressive (BVAR), vector error correction (VEC) and Bayesian error correction (BVEC) models in forecasting the exchange rates of five Central and Eastern European currencies (Czech Koruna, Hungarian Forint, Slovak Koruna, Slovenian Tolar and Polish Zloty) against the US Dollar and the Euro. Although these models tend to outperform the random walk model for long-term predictions (6 months ahead and beyond), even the best models in terms of average prediction error fail to reject the test of equality of forecasting accuracy against the random walk model in short-term predictions. Copyright © 2005 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.952
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 24 (2005)
Issue (Month): 3 ()
Pages: 189-201

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Handle: RePEc:jof:jforec:v:24:y:2005:i:3:p:189-201
DOI: 10.1002/for.952
Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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