Several authors have recently investigated the predictability of exchange rates by fitting a sequence of long-horizon error-correction equations. We show by means of a simulation study that, in small to medium samples, inference from this regression procedure depends on the null hypothesis that is used to generate empirical critical values. The standard assumption of a stationary error-correction term between exchange rates and fundamentals biases the results in favor of predictive power. Our results show that evidence of long-horizon predictability weakens when using empirical critical values generated under the more stringent null of no cointegration. Likewise, results are weakened using critical values generated under the null that exchange rates and fundamentals are generated by an unrestricted VAR with no integration restrictions. © 2000 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

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Several authors have recently investigated the predictability of exchange rates by fitting a sequence of long-horizon error-correction regressions. We show that in small to medium samples such a procedure gives rise to spurious evidence of predictive power. A simulation study demonstrates that even when using this technique on two independent series, estimates and diagnostic statistics suggest a high degree of predictability of the dependent variable. We apply a simple modification of the long-horizon regression due to Jegadeesh (1991), which may provide more accurate inference for researchers interested in comparing short and long-run predictability of U.S. dollar exchange rates.

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