Abstract
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 framework, our modeling approach assumes that different regimes are characterized by commonly used structural exchange rate models, with transitions across regimes 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 home and in the USA. 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 more accurate density forecasts for most currency pairs considered.
Highlights
Since the end of the Bretton Woods system in 1971, economists have been confronted with the challenging issue of designing empirical models of bilateral exchange rates, which are useful for forecasting applications
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 home and in the United States (USA)
Our framework allows for dynamically switching between selected theoretical exchange rate models that are used to guide the specific choice of covariates included
Summary
Since the end of the Bretton Woods system in 1971, economists have been confronted with the challenging issue of designing empirical models of bilateral exchange rates, which are useful for forecasting applications. In a seminal contribution, Meese and Rogoff (1983) provided some early evidence that exchange rates are difficult to predict, at least in the short run. Using a set of theoretical models in the spirit of Dornbusch (1976), Frankel (1979), and Hooper and Morton (1982), to guide the choice of covariates included in a forecasting regression, Meese and Rogoff (1983) found that a simple random walk benchmark is difficult to outperform for most major exchange rate pairs. Mark (1995), for instance, applied an error correction model to a set of four exchange rates against the US dollar. The finding that exchange rates tend to be predictable in the medium and long run Journal of Forecasting. 2020;39:168–186
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