Model specification and forecasting foreign exchange rates with vector autoregressions

Nathan L. Joseph

Research output: Contribution to journalArticlepeer-review


This study examines the forecasting accuracy of alternative vector autoregressive models each in a seven-variable system that comprises in turn of daily, weekly and monthly foreign exchange (FX) spot rates. The vector autoregressions (VARs) are in non-stationary, stationary and error-correction forms and are estimated using OLS. The imposition of Bayesian priors in the OLS estimations also allowed us to obtain another set of results. We find that there is some tendency for the Bayesian estimation method to generate superior forecast measures relatively to the OLS method. This result holds whether or not the data sets contain outliers. Also, the best forecasts under the non-stationary specification outperformed those of the stationary and error-correction specifications, particularly at long forecast horizons, while the best forecasts under the stationary and error-correction specifications are generally similar. The findings for the OLS forecasts are consistent with recent simulation results. The predictive ability of the VARs is very weak.
Original languageEnglish
Pages (from-to)451-484
Number of pages34
JournalJournal of Forecasting
Issue number7
Publication statusPublished - Nov 2001


  • seasonality
  • forecasting
  • cointegration
  • vector autoregressions
  • error-correction models
  • Bayesian estimation
  • outliers


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