ASSESSING FORECAST PERFORMANCE IN A COINTEGRATED SYSTEM

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 coin tegrated system we examine is composed of three vectors, a money deman d representation, a Fisher equation, and a risk premium captured by an interest rate differential. The forecasts produced by the vector erro r correction model (VECM) associated with this system are compared wit h those obtained from a corresponding differenced vector autoregressio n, (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 compa ring forecast performance for specific variables. Overall our findings suggest that selective forecast performance improvement (especially a t long forecast horizons) may be observed by incorporating knowledge o f cointegration rank. Our general conclusion is that when the advantag e of incorporating cointegration appears, it is generally at longer fo recast horizons. This is consistent with the predictions of Engle and Yoo (1987). But we also find, consistent with Clements and Hendry (199 5) that relative gain in forecast performance clearly depends upon the chosen data transformation.