Evaluating forecast accuracy for Error Correction constraints and Intercept Correction

Abstract: This paper examines the forecast accuracy of an unrestricted Vector Autoregressive (VAR) model for GDP, relative to a comparable Vector Error Correction (VEC) model that recognizes that the data is characterized by co-integration. In addition, an alternative forecast method, Intercept Correction (IC), is considered for further comparison. Recursive out-of-sample forecasts are generated for both models and forecast techniques. The generated forecasts for each model are objectively evaluated by a selection of evaluation measures and equal accuracy tests. The result shows that the VEC models consistently outperform the VAR models. Further, IC enhances the forecast accuracy when applied to the VEC model, while there is no such indication when applied to the VAR model. For certain forecast horizons there is a significant difference in forecast ability between the VEC IC model compared to the VAR model.