Editorial
In this paper, a generalized dynamic factor model is utilized to produce shortterm forecasts of real Austrian GDP. The model follows the frequency domain approach proposed by Forni, Hallin, Lippi and Reichlin (2000, 2003). The forecasting performance of the model with a large data set of 143 variables has been assessed relative to simple univariate time-series forecasts. The results show that the factor model can barely outperform the much simpler benchmark model, given the usual levels of significance. Thus the authors followed a line of research proposed by Boivin and Ng (2003) and Watson (2000), who suggested that the use of a small data set may increase the forecasting performance. The main finding from their extensive out-of-sample forecasting experiment is that the best forecasting performance can be achieved with small data sets with a handful of variables only. These models perform significantly better than the large model. This result seems to contradict the basic idea of dynamic factor models, which have been constructed to exploit the potentially useful information of a large data set.