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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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Paper provided by Oesterreichische Nationalbank (Austrian Central Bank) in its series Working Papers with number 73.

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Length: 49
Date of creation: 26 Aug 2002
Date of revision:
Handle: RePEc:onb:oenbwp:73
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  1. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
  2. repec:cup:cbooks:9780521634809 is not listed on IDEAS
  3. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Bayesian Var Models for Forecasting Irish Inflation," Research Technical Papers 4/RT/98, Central Bank of Ireland.
  4. K. Hubrich, 2001. "Forecasting euro area inflation: Does contemponaneous aggregration improve the forecasting performance," WO Research Memoranda (discontinued) 661, Netherlands Central Bank, Research Department.
  5. Hendry, David F & Michael P. Clements, 2002. "Economic Forecasting: Some Lessons from Recent Research," Royal Economic Society Annual Conference 2002 99, Royal Economic Society.
  6. Meyler, Aidan & Kenny, Geoff & Quinn, Terry, 1998. "Forecasting irish inflation using ARIMA models," MPRA Paper 11359, University Library of Munich, Germany.
  7. Clements, M.P. & Hendry, D.F., 1992. "Forecasting in Cointegrated Systems," Economics Series Working Papers 99139, University of Oxford, Department of Economics.
  8. James H. Stock & Mark W. Watson, 1999. "Forecasting Inflation," NBER Working Papers 7023, National Bureau of Economic Research, Inc.
  9. Hendry, D.F. & Mizon, G.E., 1999. "On selecting policy analysis models by forecast accuracy," Discussion Paper Series In Economics And Econometrics 9918, Economics Division, School of Social Sciences, University of Southampton.
  10. Víctor Gómez & Agustín Maravall, 1998. "Automatic Modeling Methods for Univariate Series," Banco de Espa�a Working Papers 9808, Banco de Espa�a.
  11. Bennett T. McCallum, 1989. "Targets, Indicators, and Instruments of Monetary Policy," NBER Working Papers 3047, National Bureau of Economic Research, Inc.
  12. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
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