  In this paper we aim to improve existing empirical exchange rate models by
accounting for uncertainty with respect to the underlying structural
representation. Within a flexible Bayesian non-linear time series framework,
our modeling approach assumes that different regimes are characterized by
commonly used structural exchange rate models, with their evolution being
driven by a Markov process. We assume a time-varying transition probability
matrix with transition probabilities depending on a measure of the monetary
policy stance of the central bank at the home and foreign country. We apply
this model to a set of eight exchange rates against the US dollar. In a
forecasting exercise, we show that model evidence varies over time and a model
approach that takes this empirical evidence seriously yields improvements in
accuracy of density forecasts for most currency pairs considered.
