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Forecasting euro area inflation: Does contemponaneous aggregration improve the forecasting performance

  • K. Hubrich

Since price stability is the ESCB's primary objective, the evaluation of price development in the light of the second pillar of the ESCB's monetary policy strategy is essential. As the European Central Bank has started publishing its inflation forecast for the euro area in December 2000, forecasting inflation for the area has become of increasing importance. In this study it is systematically analysed whether the forecasting performance of euro area inflation models can be improved by aggregating forecasts of HICP subindices in comparison to forecasting total euro area inflation directly. The comparison is carried out across different methodological approaches. The VECM id found to ouperform the VAR and a univariate AR model for almost all HICP (sub-)indices. The results regarding the relative performance of aggregating forecasts of disaggregated time series in comparison with forecasting the aggregated time series directly, however, show a tendency for a better performance of forecasting euro area inflation directly. Therefore, relying on aggregated forecasts of subcomponents when forecasting euro area or national inflation should be considered with some caution.

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File URL: http://www.dnb.nl/binaries/wo0661_tcm46-145963.pdf
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Paper provided by Netherlands Central Bank, Research Department in its series WO Research Memoranda (discontinued) with number 661.

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Date of creation: 2001
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Handle: RePEc:dnb:wormem:661
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Web page: http://www.dnb.nl/en/

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  1. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
  2. Gonzalo, J., 1992. "Cointegration and Aggregation," Papers 11, Boston University - Department of Economics.
  3. Clive W. J. Granger, 1988. "Aggregation of time series variables-a survey," Discussion Paper / Institute for Empirical Macroeconomics 1, Federal Reserve Bank of Minneapolis.
  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. Morana, Claudio, 2000. "Measuring core inflation in the euro area," Working Paper Series 0036, European Central Bank.
  6. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-36, January.
  7. Rose, David E., 1977. "Forecasting aggregates of independent Arima processes," Journal of Econometrics, Elsevier, vol. 5(3), pages 323-345, May.
  8. Elena Angelini & Jérôme Henry & Ricardo Mestre, 2001. "A multi-country trend indicator for euro area inflation: computation and properties," BIS Papers chapters, in: Bank for International Settlements (ed.), Empirical studies of structural changes and inflation, volume 3, pages 81-108 Bank for International Settlements.
  9. Tiao, G. C. & Guttman, Irwin, 1980. "Forecasting contemporal aggregates of multiple time series," Journal of Econometrics, Elsevier, vol. 12(2), pages 219-230, February.
  10. Lutkepohl, Helmut, 1984. "Linear transformations of vector ARMA processes," Journal of Econometrics, Elsevier, vol. 26(3), pages 283-293, December.
  11. Stock, James H, 1987. "Asymptotic Properties of Least Squares Estimators of Cointegrating Vectors," Econometrica, Econometric Society, vol. 55(5), pages 1035-56, September.
  12. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
  13. Kohn, Robert, 1982. "When is an aggregate of a time series efficiently forecast by its past?," Journal of Econometrics, Elsevier, vol. 18(3), pages 337-349, April.
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