Forecasting Austrian HICP and its Components using VAR and ARIMA Models
The purpose of this paper is to evaluate the performance of VAR and ARIMA models to forecast Austrian HICP inflation. Additionally, we investigate whether disaggregate modelling of five subcomponents of inflation is superior to specifications of headline HICP inflation. Our modelling procedure is to find adequate VAR and ARIMA specifications that minimise the 12 months out-of-sample forecasting error. The main findings are twofold. First, VAR models outperform the ARIMA models in terms of forecasting accuracy over the longer pro- jection horizon (8 to 12 months ahead). Second, a disaggregated ap- proach improves forecasting accuracy substantially for ARIMA mod- els. In case of the VAR approach the superiority of modelling the five subcomponents instead of just considering headline HICP inflation is demonstrated only over the longer period (10 to 12 months ahead).
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