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Can Parameter Instability Explain the Meese-Rogoff Puzzle?

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  • Bacchetta, Philippe
  • Beutler, Toni
  • van Wincoop, Eric

Abstract

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 is either 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.

Suggested Citation

  • Bacchetta, Philippe & Beutler, Toni & van Wincoop, Eric, 2009. "Can Parameter Instability Explain the Meese-Rogoff Puzzle?," CEPR Discussion Papers 7383, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:7383
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    Cited by:

    1. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, Elsevier.
    2. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    3. Fratzscher, Marcel & Rime, Dagfinn & Sarno, Lucio & Zinna, Gabriele, 2015. "The scapegoat theory of exchange rates: the first tests," Journal of Monetary Economics, Elsevier, vol. 70(C), pages 1-21.
    4. Byrne, Joseph P. & Korobilis, Dimitris & Ribeiro, Pinho J., 2016. "Exchange rate predictability in a changing world," Journal of International Money and Finance, Elsevier, vol. 62(C), pages 1-24.
    5. Brooks, Chris & Burke, Simon P. & Stanescu, Silvia, 2016. "Finite sample weighting of recursive forecast errors," International Journal of Forecasting, Elsevier, vol. 32(2), pages 458-474.
    6. Michalski , Tomasz & Amat , Christophe, 2014. "Fundamentals and Exchange Rate Forecastability with Machine Learning Methods," Les Cahiers de Recherche 1049, HEC Paris.
    7. Park, Cheolbeom & Park, Sookyung, 2013. "Exchange rate predictability and a monetary model with time-varying cointegration coefficients," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 394-410.
    8. Stijn Claessens & M. Ayhan Kose, 2017. "Asset prices and macroeconomic outcomes: A survey," CAMA Working Papers 2017-76, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. Tarek A. Hassan & Rui C. Mano, 2014. "Forward and Spot Exchange Rates in a Multi-currency World," NBER Working Papers 20294, National Bureau of Economic Research, Inc.
    10. Byrne, Joseph P. & Korobilis, Dimitris & Ribeiro, Pinho J., 2014. "On the Sources of Uncertainty in Exchange Rate Predictability," SIRE Discussion Papers 2015-24, Scottish Institute for Research in Economics (SIRE).
    11. Rossi, Barbara & Sekhposyan, Tatevik, 2011. "Understanding models' forecasting performance," Journal of Econometrics, Elsevier, vol. 164(1), pages 158-172, September.
    12. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    13. Bacchetta, Philippe & van Wincoop, Eric, 2013. "On the unstable relationship between exchange rates and macroeconomic fundamentals," Journal of International Economics, Elsevier, vol. 91(1), pages 18-26.
    14. Toni Beutler, 2012. "Forecasting Exchange Rates with Commodity Convenience Yields," Working Papers 12.03, Swiss National Bank, Study Center Gerzensee.
    15. Haskamp, Ulrich, 2017. "Forecasting exchange rates: The time-varying relationship between exchange rates and Taylor rule fundamentals," Ruhr Economic Papers 704, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    16. Nicholas Mangee, 2016. "Can structural change explain the Meese-Rogoff puzzle?," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 40(2), pages 211-234, April.
    17. Travis J. Berge, 2014. "Forecasting Disconnected Exchange Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 713-735, August.
    18. Lorenzo Pozzi & Barbara Sadaba, 2017. "Detecting Scapegoat Effects in the Relationship Between Exchange Rates and Macroeconomic Fundamentals," Staff Working Papers 17-22, Bank of Canada.
    19. Philippe Bacchetta & Eric van Wincoop, 2011. "Modeling Exchange Rates with Incomplete Information," Cahiers de Recherches Economiques du Département d'Econométrie et d'Economie politique (DEEP) 11.03, Université de Lausanne, Faculté des HEC, DEEP.
    20. Duncan, Roberto & Martinez-Garcia, Enrique, 2018. "New Perspectives on Forecasting Inflation in Emerging Market Economies: An Empirical Assessment," Globalization and Monetary Policy Institute Working Paper 338, Federal Reserve Bank of Dallas.

    More about this item

    Keywords

    Exchange rate forecasting; exchange rate models;

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics

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