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Can Panel Data Really Improve the Predictability of the Monetary Exchange Rate Model?

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  • Westerlund, Joakim
  • Basher, Syed A.

Abstract

A common explanation for the inability of the monetary model to beat the random walk in forecasting future exchange rates is that conventional time series tests may have low power, and that panel data should generate more powerful tests. This paper provides an extensive evaluation of this power argument to the use of panel data in the forecasting context. In particular, by using simulations it is shown that although pooling of the individual prediction tests can lead to substantial power gains, pooling only the parameters of the forecasting equation, as has been suggested in the previous literature, does not seem to generate more powerful tests. The simulation results are illustrated through an empirical application.

Suggested Citation

  • Westerlund, Joakim & Basher, Syed A., 2006. "Can Panel Data Really Improve the Predictability of the Monetary Exchange Rate Model?," MPRA Paper 1229, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:1229
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    References listed on IDEAS

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    14. Rapach, David E. & Wohar, Mark E., 2002. "Testing the monetary model of exchange rate determination: new evidence from a century of data," Journal of International Economics, Elsevier, vol. 58(2), pages 359-385, December.
    15. Papell, David H & Theodoridis, Hristos, 2001. "The Choice of Numeraire Currency in Panel Tests of Purchasing Power Parity," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 33(3), pages 790-803, August.
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    Cited by:

    1. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 995-1024, Elsevier.
    2. Chang, Ming-Jen & Matsuki, Takashi, 2022. "Exchange rate forecasting with real-time data: Evidence from Western offshoots," Research in International Business and Finance, Elsevier, vol. 59(C).
    3. Kenneth S. Rogoff & Vania Stavrakeva, 2008. "The Continuing Puzzle of Short Horizon Exchange Rate Forecasting," NBER Working Papers 14071, National Bureau of Economic Research, Inc.
    4. Narayan, Seema & Smyth, Russell, 2015. "The financial econometrics of price discovery and predictability," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 380-393.
    5. 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.
    6. Salisu, Afees A. & Vo, Xuan Vinh, 2020. "Predicting stock returns in the presence of COVID-19 pandemic: The role of health news," International Review of Financial Analysis, Elsevier, vol. 71(C).

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    More about this item

    Keywords

    Monetary Exchange Rate Model; Forecasting; Panel Data; Pooling; Bootstrap;
    All these keywords.

    JEL classification:

    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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