Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change

We conduct a detailed simulation study of the forecasting performance of
diffusion index-based methods in short samples with structural change. We
consider several data generation processes, to mimic different types of
structural change, and compare the relative forecasting performance of factor
models and more traditional time series methods. We find that changes in the
loading structure of the factors into the variables of interest are extremely
important in determining the performance of factor models. We complement
the analysis with an empirical evaluation of forecasts for the key
macroeconomic variables of the Euro area and Slovenia, for which relatively
short samples are officially available and structural changes are likely. The
results are coherent with the findings of the simulation exercise, and confirm
the relatively good performance of factor-based forecasts also in short samples
with structural change.