Working Paper: NBER ID: w28447
Authors: Charles Engel; Steve Pak Yeung Wu
Abstract: 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.
Keywords: exchange rate forecasting; dollar exchange rate; global risk measures
JEL Codes: F31; F37; G15
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| level of the log of the exchange rate (F31) | changes in the exchange rate (F31) |
| level of the exchange rate (F31) | changes in the exchange rate (F31) |
| level of the exchange rate does not significantly outperform random walk model (F31) | forecasting capability (C53) |
| measures of global risk (F65) | forecasting power (C53) |