For the past 30 years international monetary economists have
believed that exchange rate models cannot outperform the random
walk in out-of-sample forecasting as a result of the 1983 paper
written by Richard Meese and Kenneth Rogoff. Marking the
culmination of their extensive research into the Meese-Rogoff
puzzle, Moosa and Burns challenge the orthodoxy by demonstrating
that the naive random walk model can be outperformed by exchange
rate models when forecasting accuracy is measured by metrics that
do not rely exclusively on the magnitude of forecasting error. The
authors present compelling evidence, supported by their own
measure: the 'adjusted root mean square error', to finally solve
the Meese-Rogoff puzzle and provide a new alternative. Demystifying
the Meese-Rogoff Puzzle will appeal to academics with an interest
in exchange rate economics and international monetary economics. It
will also be a useful resource for central banks and financial
institutions.
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