Forecasting the U.S. Dollar in the 21st Century

The level of the (log of) the exchange rate seems to have strong forecasting power for dollar exchange rates against major currencies post-2000 at medium- to long-run horizons of 12-, 36- and 60-months. We find that this is true using conventional asymptotic statistics correcting for serial correlation biases. But correcting for small-sample bias using simulation methods, we find little evidence to reject a random walk. This small sample bias arises because of near-spurious correlation when the predictor variable is persistent and the horizon for exchange rate forecasts is long. Similar problems of spurious correlation may arise when other persistent variables are used to forecast changes in the exchange rate. We find, in fact, using asymptotic statistics, the level of the exchange rate provides better forecasts than economic measures of “global risk†, and the measures of global risk do not improve the (possibly spurious) forecasting power of the level of the exchange rate.