Can Parameter Instability Explain the Meese-Rogoff Puzzle?

Détails

ID Serval
serval:BIB_D73986152D96
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
Can Parameter Instability Explain the Meese-Rogoff Puzzle?
Titre de la conférence
NBER International Seminar on Macroeonomics 2009
Auteur(s)
Bacchetta P., van Wincoop E., Beutler T., Chinn M.D., Giannone D.
Editeur
University of Chicago Press
ISBN
0-226-70750-4
978-0-226-70749-5
Statut éditorial
Publié
Date de publication
06/2010
Peer-reviewed
Oui
Editeur scientifique
Reichlin L., West K.
Volume
6
Série
NBER Book Series
Pages
125-173
Langue
anglais
Résumé
The empirical literature on nominal exchange rates shows that the current exchange rate is often a better predictor of future exchange rates than a linear combination of macroeconomic fundamentals. This result is behind the famous Meese-Rogoff puzzle. In this paper we evaluate whether parameter instability can account for this puzzle. We consider a theoretical reduced-form relationship between the exchange rate and fundamentals in which parameters are either constant or time varying. We calibrate the model to data for exchange rates and fundamentals and conduct the exact same Meese-Rogoff exercise with data generated by the model. Our main finding is that the Impact of time-varying parameters on the prediction performance either is very small or goes in the wrong direction. To help interpret the findings, we derive theoretical results on the impact of time-varying parameters on the out-of-sample forecasting performance of the model. We conclude that it is not time-varying parameters, but rather small sample estimation bias, that explains the Meese-Rogoff puzzle.
Web of science
Création de la notice
18/02/2011 12:29
Dernière modification de la notice
20/08/2019 15:56
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