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Exchange Rate Forecasting: Results from a Threshold Autoregressive Model

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  • Michael Pippenger
  • Gregory Goering

Abstract

Structural models of exchange rate determination rarely forecast the exchange rate more accurately than a naive random walk model. Recent innovations in exchange rate modeling indicate that changes in the exchange rate may follow a self-exciting threshold autoregressive model (SETAR). We estimate a SETAR model for various monthly US dollar exchange rates and generate forecasts for the estimated models. We find: (1) nonlinearities in the data not uncovered by the standard nonlinearity tests and (2) that the SETAR model produces better forecasts than the naive random walk model. Copyright Kluwer Academic Publishers 1998

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  • Michael Pippenger & Gregory Goering, 1998. "Exchange Rate Forecasting: Results from a Threshold Autoregressive Model," Open Economies Review, Springer, vol. 9(2), pages 157-170, April.
    • Handle: RePEc:kap:openec:v:9:y:1998:i:2:p:157-170
    DOI: 10.1023/A:1008264302419
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    Cited by:

    1. Gilles Dufrenot & Laurent Mathieu & Valerie Mignon & Anne Peguin-Feissolle, 2006. "Persistent misalignments of the European exchange rates: some evidence from non-linear cointegration," Applied Economics, Taylor & Francis Journals, vol. 38(2), pages 203-229.
    2. Marcelo Griebeler, 2014. "Models for forecasting exchange rate volatility: a comparison between developed and emerging countries," Economics Bulletin, AccessEcon, vol. 34(3), pages 1618-1630.
    3. Christian Grisse & Thomas Nitschka, 2016. "Exchange Rate Returns and External Adjustment: Evidence from Switzerland," Open Economies Review, Springer, vol. 27(2), pages 317-339, April.
    4. Chin-Ping King, 2012. "Half Life of the Real Exchange Rate: Evidence from the Nonlinear Approach in Emerging Economies," Journal of Economics and Management, College of Business, Feng Chia University, Taiwan, vol. 8(1), pages 1-23, January.
    5. Michele Ca’ Zorzi & Jakub Muck & Michal Rubaszek, 2016. "Real Exchange Rate Forecasting and PPP: This Time the Random Walk Loses," Open Economies Review, Springer, vol. 27(3), pages 585-609, July.
    6. Taylor, Mark P. & Peel, David A., 2000. "Nonlinear adjustment, long-run equilibrium and exchange rate fundamentals," Journal of International Money and Finance, Elsevier, vol. 19(1), pages 33-53, February.
    7. Rotheli, Tobias F., 2002. "Bandwagon effects and run patterns in exchange rates," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 12(2), pages 157-166, April.
    8. Firat Melih Yilmaz & Ozer Arabaci, 2021. "Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 217-245, January.
    9. Johansson, Martin, 2001. "TAR models and real exchange rates," Working Papers 2001:21, Lund University, Department of Economics.
    10. Michele Ca’ Zorzi & Jakub Muck & Michal Rubaszek, 2016. "Real Exchange Rate Forecasting and PPP: This Time the Random Walk Loses," Open Economies Review, Springer, vol. 27(3), pages 585-609, July.
    11. Crespo-Cuaresma, Jesus, 2000. "Forecasting European GDP Using Self-Exciting Threshold Autoregressive Models. A Warning," Economics Series 79, Institute for Advanced Studies.

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