Quotation Hauzenberger, Niko, Huber, Florian. 2019. Model instability in predictive exchange rate regressions. Journal of Forecasting.


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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.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Journal of Forecasting
Citation Index SSCI
WU Journalrating 2009 A
WU-Journal-Rating new FIN-A, INF-A, MAR-B, STRAT-B, VW-D, WH-B
Language English
Title Model instability in predictive exchange rate regressions
Year 2019
URL https://onlinelibrary.wiley.com/doi/full/10.1002/for.2620
DOI https://doi.org/10.1002/for.2620
Open Access Y
Open Access Link https://onlinelibrary.wiley.com/doi/full/10.1002/for.2620
JEL C30, E32, E52, F31

Associations

Projects
High-dimensional statistical learning: New methods to advance economic and sustainability policies
Modeling and forecasting exchange rates in an unified econometric framework
People
Hauzenberger, Niko (Details)
Huber, Florian (Former researcher)
Organization
Institute for Macroeconomics IN (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
5323 Econometrics (Details)
5371 Macroeconomics (Details)
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