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Assessing the forecasting accuracy of alternative nominal exchange rate models: the case of long memory

✍ Scribed by David Karemera; Benjamin J. C. Kim

Publisher
John Wiley and Sons
Year
2006
Tongue
English
Weight
132 KB
Volume
25
Category
Article
ISSN
0277-6693

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✦ Synopsis

This paper presents an autoregressive fractionally integrated moving-average (ARFIMA) model of nominal exchange rates and compares its forecasting capability with the monetary structural models and the random walk model. Monthly observations are used for Canada, France, Germany, Italy, Japan and the United Kingdom for the period of April 1973 through December 1998. The estimation method is Sowell's (1992) exact maximum likelihood estimation. The forecasting accuracy of the long-memory model is formally compared to the random walk and the monetary models, using the recently developed Harvey, Leybourne and Newbold (1997) test statistics. The results show that the long-memory model is more efficient than the random walk model in stepsahead forecasts beyond 1 month for most currencies and more efficient than the monetary models in multi-step-ahead forecasts. This new finding strongly suggests that the long-memory model of nominal exchange rates be studied as a viable alternative to the conventional models.

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