Forecasting euro area inflation using time series and machine learning techniques

Machine learning models have been extensively used for forecasting inflation in individual countries, yet the number of studies focusing on the euro area is comparatively lower. The objective of this thesis is to compare the out-of-sample forecast performance of machine learning and traditional time series models on different forecast horizons for the euro area inflation. The focus is on core and headline inflation, as well as periods of high- and low-inflation. To this end, a comprehensive dataset was constructed to facilitate forecasting. The findings indicate that, in general, traditional time series models yield more accurate forecasts in the short and medium run. In contrast, machine learning models demonstrate higher forecast accuracy in the long run. This thesis also concludes that an increase in the number of predictors for machine learning models may reduce forecast performance when working with a small sample size. The findings further demonstrate that machine learning models may exhibit higher forecast accuracy for more volatile time series and during periods of high inflation. However, for forecasting core inflation in a low-inflation period, all models demonstrate similar performance, with no discernible pattern in forecast accuracy.