Editoral
In this paper, the authors apply factor models proposed by Stock and Watson and VAR and ARIMA models to generate 12-month out of sample forecasts of Austrian HICP inflation and its sub-indices processed food, unprocessed food, energy, industrial goods and services price inflation. A sequential forecast model selection procedure tailored to this specific task is applied. It turns out that factor models possess the highest predictive accuracy for several sub-indices and that predictive accuracy can be further improved by combining the information contained in factor and VAR models for some indices.
With respect to forecasting HICP inflation, the analysis suggests to favour the aggregation of sub-indices forecasts. Furthermore, the sub-indices forecasts are used as a tool to give a more detailed picture of the determinants of HICP inflation from both an ex-ante and ex-post perspective.