Model instability in predictive exchange rate regressions

Niko Hauzenberger () and Florian Huber

No 276, Department of Economics Working Paper Series from WU Vienna University of Economics and Business

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 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.

Keywords: Empirical exchange rate models; exchange rate fundamentals; Markov switching (search for similar items in EconPapers)
Date: 2018-12
New Economics Papers: this item is included in nep-ets, nep-for and nep-mon
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Model instability in predictive exchange rate regressions (2020)
Working Paper: Model instability in predictive exchange rate regressions (2018)
Working Paper: Model instability in predictive exchange rate regressions (2018)
Working Paper: Model instability in predictive exchange rate regressions (2018)
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