We consider the forecasting performance of two SETAR exchange rate models proposed by Krager and Kugler [J. Int. Money Fin. 12 (1993) 195]. Assuming that the models are good approximations to the data generating process, we show that whether the non-linearities inherent in the data can be exploited to forecast better than a random walk depends on both how forecast accuracy is assessed and on the 'state of nature'. Evaluation based on traditional measures, such as (root) mean squared forecast errors, may mask the superiority of the nonlinear models. Generalized impulse response functions are also calculated as a means of portraying the asymmetric response to shocks implied by such models. (C) 2001 Elsevier Science Ltd. All rights reserved. JEL classification: C22; F47
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