Macroeconomic forecasting: a non-standard optimisation approach to the calibration of dynamic factor models.

In this paper, we present a comparison of the forecasting perfomance of selected static and dynamic factor models on two large monthly data panels. The first dataset contains EU variables, whereas the other contains US variables. These data panels are split into two parts: the first subsample (the calibration sample) is used to select the most performing specification for each class of models in a in- sample environment and the second subsample (the proper sample) is used to compare the performances of the selected models in an out-of-sample environment. In the calibration sample, genetic algorithms are employed to achieve an efficient exploration of the parameter space. We find that dynamic factor models are globally the most performing methods on both data panels.