Working Paper: CEPR ID: DP6706
Authors: Anindya Banerjee; Massimiliano Marcellino; Igor Masten
Abstract: 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 in short samples with structural change.
Keywords: factor models; forecasts; parameter uncertainty; short samples; structural change; time series models
JEL Codes: C32; C53; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| changes in the loading structure of factors into the variables (C29) | performance of factor models (C52) |
| diffusion index-based forecasts (C43) | outperform traditional time series models (C22) |
| number of factors used and their respective persistence parameters (C38) | performance of factor models (C52) |
| structural changes (L16) | performance of factor models (C52) |