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Forecasting Austrian inflation

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Author Info

  • Moser, Gabriel
  • Rumler, Fabio
  • Scharler, Johann

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.

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Bibliographic Info

Article provided by Elsevier in its journal Economic Modelling.

Volume (Year): 24 (2007)
Issue (Month): 3 (May)
Pages: 470-480

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Handle: RePEc:eee:ecmode:v:24:y:2007:i:3:p:470-480

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Web page: http://www.elsevier.com/locate/inca/30411

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References

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  1. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
  2. H Stekler & R A Fildes, 1999. "The state of macroeconomic forecasting," Working Papers 539557, Lancaster University Management School, Economics Department.
  3. K. Hubrich, 2001. "Forecasting euro area inflation: Does contemponaneous aggregration improve the forecasting performance," WO Research Memoranda (discontinued) 661, Netherlands Central Bank, Research Department.
  4. 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.
  5. Benalal, Nicholai & Diaz del Hoyo, Juan Luis & Landau, Bettina & Roma, Moreno & Skudelny, Frauke, 2004. "To aggregate or not to aggregate? Euro area inflation forecasting," Working Paper Series 0374, European Central Bank.
  6. Marc-André Gosselin & Greg Tkacz, 2001. "Evaluating Factor Models: An Application to Forecasting Inflation in Canada," Working Papers 01-18, Bank of Canada.
  7. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  8. Massimiliano Marcellino & James H. Stock & Mark W. Watson, . "Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information," Working Papers 201, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  9. Todd E. Clark & Michael W. McCracken, 2001. "Evaluating long-horizon forecasts," Research Working Paper RWP 01-14, Federal Reserve Bank of Kansas City.
  10. Moser, Gabriel & Scharler, Johann & Fritzer, Friedrich, 2002. "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers 73, Oesterreichische Nationalbank (Austrian Central Bank).
  11. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
  12. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  13. James H. Stock & Mark W. Watson, 1999. "Forecasting Inflation," NBER Working Papers 7023, National Bureau of Economic Research, Inc.
  14. Francis X. Diebold & Jose A. Lopez, 1996. "Forecast Evaluation and Combination," NBER Technical Working Papers 0192, National Bureau of Economic Research, Inc.
  15. Hubrich, Kirstin, 2003. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," Working Paper Series 0247, European Central Bank.
  16. Kunst, Robert M., 2003. "Testing for Relative Predictive Accuracy: A Critical Viewpoint," Economics Series 130, Institute for Advanced Studies.
  17. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
  18. 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.
  19. Ashley, Richard, 2003. "Statistically significant forecasting improvements: how much out-of-sample data is likely necessary?," International Journal of Forecasting, Elsevier, vol. 19(2), pages 229-239.
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Citations

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Cited by:
  1. D'Agostino, Antonello & Bermingham, Colin, 2010. "Understanding and Forecasting Aggregate and Disaggregate Price Dynamics," Research Technical Papers 8/RT/10, Central Bank of Ireland.
  2. Ibarra, Raul, 2012. "Do disaggregated CPI data improve the accuracy of inflation forecasts?," Economic Modelling, Elsevier, vol. 29(4), pages 1305-1313.
  3. Peter Vlaar & Ard den Reijer, 2004. "Forecasting inflation: An art as well as a science!," Computing in Economics and Finance 2004 148, Society for Computational Economics.
  4. Hendry, David F. & Hubrich, Kirstin, 2010. "Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate," Working Paper Series 1155, European Central Bank.
  5. 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.
  6. 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.
  7. Gilles Mourre & Michael Thiel, 2006. "Monitoring short-term labour cost developments in the European Union: which indicators to trust?," European Economy - Economic Papers 258, Directorate General Economic and Monetary Affairs (DG ECFIN), European Commission.
  8. 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.
  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.

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