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Forecasting Austrian Inflation

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Abstract

In this paper we apply factor models proposed by Stock and Watson [18] 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.

Suggested Citation

  • Gabriel Moser & Fabio Rumler & Johann Scharler, 2004. "Forecasting Austrian Inflation," Working Papers 91, Oesterreichische Nationalbank (Austrian Central Bank).
  • Handle: RePEc:onb:oenbwp:91
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    1. Elena Angelini & Jérôme Henry & Ricardo Mestre, 2001. "Diffusion index-based inflation forecasts for the euro area," BIS Papers chapters,in: Bank for International Settlements (ed.), Empirical studies of structural changes and inflation, volume 3, pages 109-138 Bank for International Settlements.
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    7. Fildes, Robert & Stekler, Herman, 2002. "Reply to the comments on 'The state of macroeconomic forecasting'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 503-505, December.
    8. Marc-André Gosselin & Greg Tkacz, 2001. "Evaluating Factor Models: An Application to Forecasting Inflation in Canada," Staff Working Papers 01-18, Bank of Canada.
    9. K. Hubrich, 2001. "Forecasting euro area inflation: Does contemponaneous aggregration improve the forecasting performance," WO Research Memoranda (discontinued) 661, Netherlands Central Bank, Research Department.
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    14. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
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    Cited by:

    1. Colin Bermingham & Antonello D’Agostino, 2014. "Understanding and forecasting aggregate and disaggregate price dynamics," Empirical Economics, Springer, vol. 46(2), pages 765-788, March.
    2. Rumler, Fabio & Valderrama, Maria Teresa, 2010. "Comparing the New Keynesian Phillips Curve with time series models to forecast inflation," The North American Journal of Economics and Finance, Elsevier, vol. 21(2), pages 126-144, August.
    3. Ard Reijer & Peter Vlaar, 2006. "Forecasting Inflation: An Art as Well as a Science!," De Economist, Springer, vol. 154(1), pages 19-40, March.
    4. repec:eee:ecmode:v:66:y:2017:i:c:p:201-213 is not listed on IDEAS
    5. 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.
    6. Tallman, Ellis W. & Zaman, Saeed, 2017. "Forecasting inflation: Phillips curve effects on services price measures," International Journal of Forecasting, Elsevier, vol. 33(2), pages 442-457.
    7. Mihaela SIMIONESCU, 2014. "Improving The Inflation Rate Forecasts Of Romanian Experts Using A Fixed-Effects Models Approach," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 13, pages 87-102, June.
    8. Gilles Mourre & Michael Thiel, 2006. "Monitoring short-term labour cost developments in the European Union: which indicators to trust?," European Economy - Economic Papers 2008 - 2015 258, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    9. 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.
    10. Hendry, David F. & Hubrich, Kirstin, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 216-227.
    11. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    12. Ibarra, Raul, 2012. "Do disaggregated CPI data improve the accuracy of inflation forecasts?," Economic Modelling, Elsevier, vol. 29(4), pages 1305-1313.
    13. Horváth, Roman, 2009. "The time-varying policy neutral rate in real-time: A predictor for future inflation?," Economic Modelling, Elsevier, vol. 26(1), pages 71-81, January.
    14. 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

    Inflation Forecasting; Forecast Model selection; Aggregation;

    JEL classification:

    • 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

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