Forecasting the Exchange Rate using Non-linear Taylor Rule Based Models

Rudan Wang, Bruce Morley, Michalis Stamatogiannis

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15 Citations (SciVal)
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This research utilises a non-linear Smooth Transition Regression (STR) approach to modelling and forecasting the exchange rate, based on the Taylor rule model of exchange rate determination. The separate literatures
on exchange rate models and the Taylor rule have already shown that the non-linear specification can outperform the equivalent linear one. In addition the Taylor rule based exchange rate model used here has been
augmented with a wealth effect to reflect the increasing importance of the asset markets in monetary policy. Using STR models, the results offer evidence of non-linearity in the variables used and that the interest rate
differential is the most appropriate transition variable. We conduct the conventional out-of-sample forecasting performance test, which indicates that the non-linear models outperform their linear equivalents as well as
the non-linear UIP model and random walk.
Original languageEnglish
Pages (from-to)429-442
Number of pages14
JournalInternational Journal of Forecasting
Issue number2
Early online date28 Dec 2018
Publication statusPublished - 1 Apr 2019


  • Exchange rates, forecasting, Taylor rule, wealth effect, Smooth Transition

ASJC Scopus subject areas

  • Economics, Econometrics and Finance (miscellaneous)


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