A selective procedure for combining forecasts

If there are various forecasts for the same random variable, it is common practice to combine these forecasts in order to obtain a better forecast. But an important question is how to perform the combination, especially if the system under investigation is subject to structural changes and consequently the best combination method is not the same all of the time. This paper presents a data driven approach, which for each point of time selects a combination technique from a given set of combination techniques. Properties and limitations of this selection procedure are investigated using simulated data from normal distributions.