Forecasting Austrian Inflation
AbstractIn this paper we 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 subindices 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 subindices 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, our analysis suggests to favor the aggregation of subindices forecasts. Furthermore, the subindices 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.
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Bibliographic InfoPaper provided by Oesterreichische Nationalbank (Austrian Central Bank) in its series Working Papers with number 91.
Date of creation: 04 Oct 2004
Date of revision:
Postal: Oesterreichische Nationalbank, Economic Studies Division, c/o Beate Hofbauer-Berlakovich, POB 61, A-1011 Vienna, Austria
Other versions of this item:
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-01-16 (All new papers)
- NEP-CBA-2005-01-16 (Central Banking)
- NEP-ECM-2005-01-16 (Econometrics)
- NEP-ETS-2005-01-16 (Econometric Time Series)
- NEP-MAC-2005-01-16 (Macroeconomics)
- NEP-MON-2005-01-16 (Monetary Economics)
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