In this paper we evaluate the relative performance of linear, non-linear and time-varying models for about 500 macroeconomic variables for the countries in the Euro area, using real-time forecasting methodology. It turns out that linear models work well for about 35% of the series under analysis, time-varying models for another 35% and on-linear models for the remaining 30% of the series. The gains in forecasting accuracy from the choice of the best model can be substantial, in particular for longer forecast horizons.These results emerge from a detailed is aggregated analysis, while they are hidden when an average loss function is used. To explore in more detail the issue of parameter instability, we then apply a battery of tests, detecting non-constancy in about 20-30% of the time series. For these variables the forecasting performance of the time-varying and non-linear models further improves, with larger gains for a larger fraction of the series. Finally, we evaluate whether non-linear models perform better for three key macroeconomic variables: industrial production, inflation and unemployment. It turns out that this is often the case. Hence, overall, our results indicate that there is a substantial amount of instability and non-linearity in the EMU, and suggest that it can be worth going beyond linear models for several EMU macroeconomic variables.
