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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.