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Forecasting Exchange Rates Out-of-Sample with Panel Methods and Real-Time Data

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  • Onur Ince

Abstract

This paper evaluates out-of-sample exchange rate forecasting with Purchasing Power Parity (PPP) and Taylor rule fundamentals for 9 OECD countries vis-à-vis the U.S. dollar over the period from 1973:Q1 to 2009:Q1 at short and long horizons. In contrast with previous work, which reports “forecasts” using revised data, I construct a quarterly real-time dataset that incorporates only the information available to market participants when the forecasts are made. Using bootstrapped out-of-sample test statistics, the exchange rate model with Taylor rule fundamentals performs better at the one-quarter horizon and panel estimation is not able to improve its performance. The PPP model, however, forecasts better at the 16-quarter horizon and its performance increases in panel framework. The results are in accord with previous research on long-run PPP and Taylor rule models. Key Words: Exchange Rate Forecasting, Taylor Rules, Real-Time Data, Out-of-Sample Test Statistics

Suggested Citation

  • Onur Ince, 2013. "Forecasting Exchange Rates Out-of-Sample with Panel Methods and Real-Time Data," Working Papers 13-04, Department of Economics, Appalachian State University.
  • Handle: RePEc:apl:wpaper:13-04
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    Cited by:

    1. Byrne, Joseph P. & Korobilis, Dimitris & Ribeiro, Pinho J., 2016. "Exchange rate predictability in a changing world," Journal of International Money and Finance, Elsevier, pages 1-24.
    2. Galimberti, Jaqueson K. & Moura, Marcelo L., 2013. "Taylor rules and exchange rate predictability in emerging economies," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 1008-1031.
    3. Michele Ca’ Zorzi & Jakub Muck & Michal Rubaszek, 2016. "Real Exchange Rate Forecasting and PPP: This Time the Random Walk Loses," Open Economies Review, Springer, pages 585-609.
    4. Dick, Christian D. & MacDonald, Ronald & Menkhoff, Lukas, 2015. "Exchange rate forecasts and expected fundamentals," Journal of International Money and Finance, Elsevier, vol. 53(C), pages 235-256.
    5. Annalisa Ferrando & Klaas Mulier, 2015. "Firms’ Financing Constraints: Do Perceptions Match the Actual Situation?," The Economic and Social Review, Economic and Social Studies, pages 87-117.
    6. repec:eee:finana:v:52:y:2017:i:c:p:309-315 is not listed on IDEAS
    7. Ince, Onur & Molodtsova, Tanya & Papell, David H., 2016. "Taylor rule deviations and out-of-sample exchange rate predictability," Journal of International Money and Finance, Elsevier, vol. 69(C), pages 22-44.
    8. repec:sgh:erfinj:v:2:y:2017:i:1:p:1-21 is not listed on IDEAS
    9. repec:eee:inecon:v:107:y:2017:i:c:p:127-146 is not listed on IDEAS
    10. N. Cassola & F. Koulischer, 2016. "The Collateral Channel of Open Market Operations," Working papers 593, Banque de France.
    11. Patrick Minford & Yongdeng Xu & Peng Zhou, 2015. "How Good are Out of Sample Forecasting Tests on DSGE Models?," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), pages 333-351.
    12. Ca’ Zorzi, Michele & Kolasa, Marcin & Rubaszek, Michał, 2017. "Exchange rate forecasting with DSGE models," Journal of International Economics, Elsevier, pages 127-146.
    13. Michele Ca’ Zorzi & Jakub Muck & Michal Rubaszek, 2016. "Real Exchange Rate Forecasting and PPP: This Time the Random Walk Loses," Open Economies Review, Springer, pages 585-609.
    14. Kumar, Anil & M. Orrenius, Pia, 2016. "A closer look at the Phillips curve using state-level data," Journal of Macroeconomics, Elsevier, pages 84-102.
    15. Ince, Onur & Molodtsova, Tanya, 2017. "Rationality and forecasting accuracy of exchange rate expectations: Evidence from survey-based forecasts," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 47(C), pages 131-151.
    16. Dent, Kieran & Hacioglu Hoke, Sinem & Panagiotopoulos, Apostolos, 2017. "Solvency and wholesale funding cost interactions at UK banks," Bank of England working papers 681, Bank of England.

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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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