Mr. Francis X. Diebold and Mr. Peter F. Christoffersen
Imposing cointegration on a forecasting system, if cointegration is present, is believed to improve long-horizon forecasts. Contrary to this belief, at long horizons nothing is lost by ignoring cointegration when the forecasts are evaluated using standard multivariate forecast accuracy measures. In fact, simple univariate Box-Jenkins forecasts are just as accurate. Our results highlight a potentially important deficiency of standard forecast accuracy measures—they fail to value the maintenance of cointegrating relationships among variables—and we suggest alternatives that explicitly do so.
Assessing the magnitude of the output gap is critical to achieving an optimal policy mix. Unfortunately, the gap is an unobservable variable, which, in practice, has been estimated in a variety of ways, depending on the preferences of the modeler. This model selection problem leads to a substantial degree of uncertainty regarding the magnitude of the output gap, which can reduce its usefulness as a policy tool. To overcome this problem, in this paper we attempt to insert some discipline into this search by providing two metrics-inflation forecasting and business cycle dating-against which different options can be evaluated using aggregated euro-area GDP data. Our results suggest that Gali, Gertler, and Lopez-Salido's (2001) inefficiency wedge performs best in inflation forecasting and production function methodology dominates in the prediction of turning points. If, however, a unique methodology must be selected, the quadratic trend delivers the best overall results.
This paper develops a new forecasting framework for GDP growth in Korea to complement and further enhance existing forecasting approaches. First, a range of forecast models, including indicator- and pure time-series models, are evaluated for their forecasting performance. Based on the evaluation results, a new forecasting framework is developed for GDP projections. The framework also generates a data-driven reference band for the projections, and is therefore convenient to update. The framework is applied to the current World Economic Outlook (WEO) forecast period and the Great Recession to compare its performance to past projections. Results show that the performance of the new framework often improves the forecasts, especially at quarterly frequency, and the forecasting exercise will be better informed by cross-checking with the new data-driven framework projections.