This paper examines the forecast performance of a cointegrated system relative to the forecast performance of a comparable VAR that fails to recognize that the system is characterized by cointegration. The cointegrated system we examine is composed of three vectors, a money demand representation, a Fisher equation, and a risk premium captured by an interest rate differential. The forecasts produced by the vector error correction model (VECM) associated with this system are compared with those obtained from a corresponding differenced vector autoregression, (DVAR) as well as a vector autoregression based upon the levels of the data (LVAR). Forecast evaluation is conducted using both the full-system criterion proposed by Clements and Hendry (1993) and by comparing forecast performance for specific variables. Overall our findings suggest that selective forecast performance improvement (especially at long forecast horizons) may be observed by incorporating knowledge of cointegration rank. Our general conclusion is that when the advantage of incorporating cointegration appears, it is generally at longer forecast horizons. This is consistent with the predictions of Engle and Yoo (1987). But we also find, consistent with Clements and Hendry (1995) that relative gain in forecast performance clearly depends upon the chosen data transformation.
