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In this paper we aim to improve existing empirical exchange rate models byaccounting for uncertainty with respect to the underlying structuralrepresentation. Within a flexible Bayesian non-linear time series framework,our modeling approach assumes that different regimes are characterized bycommonly used structural exchange rate models, with their evolution beingdriven by a Markov process. We assume a time-varying transition probabilitymatrix with transition probabilities depending on a measure of the monetarypolicy stance of the central bank at the home and foreign country. We applythis model to a set of eight exchange rates against the US dollar. In aforecasting exercise, we show that model evidence varies over time and a modelapproach that takes this empirical evidence seriously yields improvements inaccuracy of density forecasts for most currency pairs considered.

Model instability in predictive exchange rate regressions