Can Cointegration-based Forecasting Outperform Univariate Models? An Application to Asian Exchange Rates

摘要

Conventional wisdom holds that restrictions on low-frequency dynamics among cointegrated variables should provide more accurate short- to medium-term forecasts than univariate techniques that contain no such information; even though, on standard accuracy measures, the information may not improve long-term forecasting. But inconclusive empirical evidence is complicated by confusion about an appropriate accuracy criterion and the role of integration and cointegration in forecasting accuracy. We evaluate the short- and medium-term forecasting accuracy of univariate Box-Jenkins type ARIMA techniques that imply only integration against multivariate cointegration models that contain both integration and cointegration for a system of five cointegrated Asian exchange rate time series. We use a rolling-window technique to make multiple out of sample forecasts from one to forty steps ahead. Relative forecasting accuracy for individual exchange rates appears to be sensitive to the behaviour of the exchange rate series and the forecast horizon length. Over short horizons, ARIMA model forecasts are more accurate for series with moving-average terms of order >1. ECMs perform better over medium-term time horizons for series with no moving average terms. The results suggest a need to distinguish between 'sequential' and 'synchronous' forecasting ability in such comparisons.