Model instability in predictive exchange rate regressions

Niko Hauzenberger, Florian Huber

Publication: Scientific journalJournal articlepeer-review


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.
Original languageEnglish
JournalJournal of Forecasting
Publication statusPublished - 2019

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 502025 Econometrics
  • 502018 Macroeconomics

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