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Forecasting Economic Aggregates by Disaggregates

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Author Info
Hendry, David F
Hubrich, Kirstin

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Abstract

We explore whether forecasting an aggregate variable using information on its disaggregate components can improve the prediction mean squared error over first forecasting the disaggregates and then aggregating those forecasts, or, alternatively, over using only lagged aggregate information in forecasting the aggregate. We show theoretically that the first method of forecasting the aggregate should outperform the alternative methods in population. We investigate whether this theoretical prediction can explain our empirical findings and analyse why forecasting the aggregate using information on its disaggregate components improves forecast accuracy of the aggregate forecast of euro area and US inflation in some situations, but not in others.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 5485.

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Date of creation: Jan 2006
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Handle: RePEc:cpr:ceprdp:5485

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Related research
Keywords: disaggregate information; factor models; forecast model selection; predictability; VAR;

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Find related papers by JEL classification:
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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References listed on IDEAS
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  1. Diebold, Francis X & Kilian, Lutz, 2000. "Unit-Root Tests Are Useful for Selecting Forecasting Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 265-73, July.
    Other versions:
  2. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2003. "The Generalized Dynamic Factor Model. One-Sided Estimation and Forecasting," LEM Papers Series 2003/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy. [Downloadable!]
    Other versions:
  3. 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. [Downloadable!] (restricted)
  4. Clements, Michael P. & Hendry, David F., 2006. "Forecasting with Breaks," Handbook of Economic Forecasting, Elsevier. [Downloadable!] (restricted)
  5. David F. Hendry, 2004. "Unpredictability and the Foundations of Economic Forecasting," Economics Papers 2004-W15, Economics Group, Nuffield College, University of Oxford. [Downloadable!]
  6. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December. [Downloadable!] (restricted)
  7. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
    Other versions:
  8. Raffaella Giacomini & Halbert White, 2003. "Tests of Conditional Predictive Ability," Econometrics 0308001, EconWPA. [Downloadable!]
    Other versions:
  9. Clive W. J. Granger, 1988. "Aggregation of time series variables-a survey," Discussion Paper / Institute for Empirical Macroeconomics 1, Federal Reserve Bank of Minneapolis. [Downloadable!]
  10. Guillaume Chevillon, 2005. "Direct multi-step estimation and forecasting," Documents de Travail de l'OFCE 2005-10, Observatoire Francais des Conjonctures Economiques (OFCE). [Downloadable!]
    Other versions:
  11. Pesaran, M Hashem & Pierse, Richard G & Kumar, Mohan S, 1989. "Econometric Analysis of Aggregation in the Context of Linear Prediction Models," Econometrica, Econometric Society, vol. 57(4), pages 861-88, July. [Downloadable!] (restricted)
    Other versions:
  12. 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. [Downloadable!] (restricted)
    Other versions:
  13. 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. [Downloadable!] (restricted)
  14. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October. [Downloadable!] (restricted)
    Other versions:
  15. David F. Hendry & Michael P. Clements, 2004. "Pooling of forecasts," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, 06. [Downloadable!] (restricted)
    Other versions:
  16. Lutkepohl, Helmut, 1984. "Forecasting Contemporaneously Aggregated Vector ARMA Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 201-14, July.
  17. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-89, June. [Downloadable!] (restricted)
  18. Todd E. Clark, 1996. "Finite-sample properties of tests for forecast equivalence," Research Working Paper 96-03, Federal Reserve Bank of Kansas City. [Downloadable!]
  19. Graham Elliott & Allan Timmermann, 2008. "Economic Forecasting," Journal of Economic Literature, American Economic Association, vol. 46(1), pages 3-56, March.
    Other versions:
  20. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis. [Downloadable!]
  21. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2006. "A quasi maximum likelihood approach for large approximate dynamic factor models," Working Paper Series 674, European Central Bank. [Downloadable!]
    Other versions:
  22. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November. [Downloadable!] (restricted)
    Other versions:
  23. David F. Hendry, 2004. "Unpredictability and the Foundations of Economic Forecasting," Econometric Society 2004 Australasian Meetings 27, Econometric Society.
  24. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  25. Clements, Michael P. & Hendry, David F., 1996. "Multi-Step Estimation for Forecasting," The Warwick Economics Research Paper Series (TWERPS) 447, University of Warwick, Department of Economics.
    Other versions:
  26. Marcellino, Massimiliano & Stock, James H & Watson, Mark W, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," CEPR Discussion Papers 4976, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  27. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583. [Downloadable!] (restricted)
  28. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531895, January.
  29. Inoue, Atsushi & Kilian, Lutz, 2003. "On the Selection of Forecasting Models," CEPR Discussion Papers 3809, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  30. Francis X. Diebold & Lutz Kilian, 2001. "Measuring predictability: theory and macroeconomic applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(6), pages 657-669. [Downloadable!]
    Other versions:
  31. A. Espasa & E. Senra & R. Albacete, 2002. "Forecasting inflation in the European Monetary Union: A disaggregated approach by countries and by sectors," European Journal of Finance, Taylor and Francis Journals, vol. 8(4), pages 402-421, December. [Downloadable!] (restricted)
  32. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
    Other versions:
  33. van Garderen, Kees Jan & Lee, Kevin & Pesaran, M. Hashem, 2000. "Cross-sectional aggregation of non-linear models," Journal of Econometrics, Elsevier, vol. 95(2), pages 285-331, April. [Downloadable!] (restricted)
    Other versions:
  34. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January. [Downloadable!] (restricted)
    Other versions:
Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Carlo Altavilla & Matteo Ciccarelli, 2006. "Inflation Forecasts, Monetary Policy and Unemployment Dynamics: Evidence from the US and the Euro Area," Discussion Papers 7_2006, D.E.S. (Department of Economic Studies), University of Naples "Parthenope", Italy. [Downloadable!]
    Other versions:
  2. 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. [Downloadable!]
  3. Gomez, Miguel I. & Gonzalez, Eliana & Melo, Luis F. & Torres, Jose L., 2006. "Forecasting Food Price Inflation, Challenges for Central Banks in Developing Countries using an Inflation Targeting Framework: the Case of Colombia," 2006 Annual meeting, July 23-26, Long Beach, CA 21181, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association). [Downloadable!]
  4. Stéphane Dées & Matthias Burgert, 2008. "Forecasting world trade. Direct versus "bottom-up" approaches," Working Paper Series 882, European Central Bank. [Downloadable!]
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  5. Emil Stavrev, 2006. "Measures of Underlying Inflation in the Euro Area: Assessment and Role for Informing Monetary Policy," IMF Working Papers 06/197, International Monetary Fund. [Downloadable!]
  6. Eliana González & Miguel I. Gómez & Luis F. Melo & José Luis Torres, 2006. "Forecasting Food Price Inflation in Developing Countries with Inflation Targeting Regimes: the Colombian Case," BORRADORES DE ECONOMIA 002735, BANCO DE LA REPÚBLICA. [Downloadable!]
  7. Ard den Reijer, 2007. "Identifying Regional and Sectoral Dynamics of the Dutch Staffing Labour Cycle," DNB Working Papers 153, Netherlands Central Bank, Research Department. [Downloadable!]
  8. J. James Reade & Ulrich Volz, 2009. "Leader of the Pack? German Monetary Dominance in Europe Prior to EMU," Economics Series Working Papers 419, University of Oxford, Department of Economics. [Downloadable!]
  9. Günter W. Beck & Kirstin Hubrich & Massimiliano Marcellino, 2006. "Regional inflation dynamics within and across euro area countries and a comparison with the US," Working Paper Series 681, European Central Bank. [Downloadable!]
  10. Guenter Beck & Massimiliano Marcellino, 2006. "Regional Inflation Dynamics within and across Euro Area and a Comparison with the US," Computing in Economics and Finance 2006 338, Society for Computational Economics. [Downloadable!]
  11. Badi H. Baltagi, 2007. "Forecasting with Panel Data," Center for Policy Research Working Papers 91, Center for Policy Research, Maxwell School, Syracuse University. [Downloadable!]
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