Abstract

We use factor-augmented predictive regression to analyze the relation between nominal exchange rates and macroeconomic variables. Using a panel of 127 US macroeconomic time series, we estimate eight factors through principal component analysis. Those estimated factors have significant predictive power and can substantially improve the predictive power of Purchasing Power Parity through both in-sample and out-of-sample analyses. The estimated macroeconomic factor, which comoves with US money supply measures, has strong predictive power for nominal exchange rate fluctuations in the short run, while estimated factors, comoving with interest rate spreads and employment variables, have strong predictive power in the long run. Moreover, optimal factors selected by the BIC in the out-of-sample analysis differ greatly depending on the time points when forecasts are made. Finally, we show that factors extracted from a panel of 127 US time series data and those extracted from a panel of 215 Korean macroeconomic series together can predict a substantial portion of movements in the Korea-US bilateral exchange rate.

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