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Model instability in predictive exchange rate regressions

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 exc...

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Detalles Bibliográficos
Autores principales: Hauzenberger, Niko, Huber, Florian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7043380/
https://www.ncbi.nlm.nih.gov/pubmed/32139954
http://dx.doi.org/10.1002/for.2620
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author Hauzenberger, Niko
Huber, Florian
author_facet Hauzenberger, Niko
Huber, Florian
author_sort Hauzenberger, Niko
collection PubMed
description 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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spelling pubmed-70433802020-03-03 Model instability in predictive exchange rate regressions Hauzenberger, Niko Huber, Florian J Forecast Research Articles 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. John Wiley and Sons Inc. 2019-12-03 2020-03 /pmc/articles/PMC7043380/ /pubmed/32139954 http://dx.doi.org/10.1002/for.2620 Text en © 2019 The Authors Journal of Forecasting Published by John Wiley & Sons Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Hauzenberger, Niko
Huber, Florian
Model instability in predictive exchange rate regressions
title Model instability in predictive exchange rate regressions
title_full Model instability in predictive exchange rate regressions
title_fullStr Model instability in predictive exchange rate regressions
title_full_unstemmed Model instability in predictive exchange rate regressions
title_short Model instability in predictive exchange rate regressions
title_sort model instability in predictive exchange rate regressions
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7043380/
https://www.ncbi.nlm.nih.gov/pubmed/32139954
http://dx.doi.org/10.1002/for.2620
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