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Forecasting Austrian HICP and its Components using VAR and ARIMA Models

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Abstract

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).

Suggested Citation

  • Friedrich Fritzer & Gabriel Moser & Johann Scharler, 2002. "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers 73, Oesterreichische Nationalbank (Austrian Central Bank).
  • Handle: RePEc:onb:oenbwp:73
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    References listed on IDEAS

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    Cited by:

    1. repec:onb:oenbwp:y::i:91:b:1 is not listed on IDEAS
    2. Ard Reijer & Peter Vlaar, 2006. "Forecasting Inflation: An Art as Well as a Science!," De Economist, Springer, pages 19-40.
    3. Janine Aron & John Muellbauer, 2008. "New methods for forecasting inflation and its sub-components: application to the USA," Economics Series Working Papers 406, University of Oxford, Department of Economics.
    4. Duarte, Claudia & Rua, Antonio, 2007. "Forecasting inflation through a bottom-up approach: How bottom is bottom?," Economic Modelling, Elsevier, vol. 24(6), pages 941-953, November.
    5. Aron, Janine & Muellbauer, John, 2012. "Improving forecasting in an emerging economy, South Africa: Changing trends, long run restrictions and disaggregation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 456-476.
    6. Marco Huwiler & Daniel Kaufmann, 2013. "Combining disaggregate forecasts for inflation: The SNB's ARIMA model," Economic Studies 2013-07, Swiss National Bank.
    7. Andreja Pufnik & Davor Kunovac, 2006. "Short-Term Forecasting of Inflation in Croatia with Seasonal ARIMA Processes," Working Papers 16, The Croatian National Bank, Croatia.
    8. Moser, Gabriel & Rumler, Fabio & Scharler, Johann, 2007. "Forecasting Austrian inflation," Economic Modelling, Elsevier, vol. 24(3), pages 470-480, May.
    9. O. De Bandt & E. Michaux & C. Bruneau & A. Flageollet, 2007. "Forecasting inflation using economic indicators: the case of France," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(1), pages 1-22.
    10. Roma, Moreno & Skudelny, Frauke & Benalal, Nicholai & Diaz del Hoyo, Juan Luis & Landau, Bettina, 2004. "To aggregate or not to aggregate? Euro area inflation forecasting," Working Paper Series 374, European Central Bank.
    11. A.H.J. den Reijer & P.J.G. Vlaar, 2003. "Forecasting Inflation in the Netherlands and the Euro Area," WO Research Memoranda (discontinued) 723, Netherlands Central Bank, Research Department.
    12. Aron, Janine & Muellbauer, John, 2010. "Does aggregating forecasts by CPI component improve inflation forecast accuracy in South Africa?," CEPR Discussion Papers 7895, C.E.P.R. Discussion Papers.

    More about this item

    Keywords

    VAR and ARIMA models; in ation forecasting; automatic modelling; forecasting accuracy.;

    JEL classification:

    • 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

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