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Granger Causality from Exchange Rates to Fundamentals: What Does the Bootstrap Test Show Us?

Author

Listed:
  • Hsiu-Hsin Ko

    (National University of Kaohsiung)

  • Masao Ogaki

Abstract

We use a residual-based bootstrap method to re-examine the finding of the Granger causality relationship from exchange rates to fundamentals in Engel and West (Exchange rate and fundamentals, Journal of Political Economy 2005, 113 (3), 485\96517), in which the evidence for the relation is taken as evidence for the present-value model for exchange rates. The test results are against the previous findings. The Monte Carlo experiment results suggest that the causality test implemented in the previous study tends to spuriously reject null hypotheses. Thus, the existing evidence for the present value model for exchange rates is not robust.

Suggested Citation

  • Hsiu-Hsin Ko & Masao Ogaki, 2013. "Granger Causality from Exchange Rates to Fundamentals: What Does the Bootstrap Test Show Us?," RCER Working Papers 577, University of Rochester - Center for Economic Research (RCER).
  • Handle: RePEc:roc:rocher:577
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    References listed on IDEAS

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    Cited by:

    1. Lansing, Kevin J. & Ma, Jun, 2017. "Explaining exchange rate anomalies in a model with Taylor-rule fundamentals and consistent expectations," Journal of International Money and Finance, Elsevier, vol. 70(C), pages 62-87.
    2. Ko, Hsiu-Hsin & Ogaki, Masao, 2015. "Granger causality from exchange rates to fundamentals: What does the bootstrap test show us?," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 198-206.

    More about this item

    Keywords

    Bootstrap; Granger causality; exchange rates; fundamentals;

    JEL classification:

    • F30 - International Economics - - International Finance - - - General
    • 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

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