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Forecasting Aggregates by Disaggregates

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  • Kirstin Hubrich
  • David F. Hendry

    ()
    (Research Department European Central Bank)

Abstract

We explore whether forecasting an aggregate variable using information on its disaggregate components can improve the prediction mean squared error over forecasting the disaggregates and aggregating those forecasts, or using only aggregate information in forecasting the aggregate. An implication of a general theory of prediction is that the first should outperform the alternative methods to forecasting the aggregate in population. However, forecast models are based on sample information. The data generation process and the forecast model selected might differ. We show how changes in collinearity between regressors affect the bias-variance trade-off in model selection and how the criterion used to select variables in the forecasting model affects forecast accuracy. We investigate why forecasting the aggregate using information on its disaggregate components improves forecast accuracy of the aggregate forecast of Euro area inflation in some situations, but not in others.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2005 with number 270.

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Date of creation: 11 Nov 2005
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Handle: RePEc:sce:scecf5:270

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Keywords: Disaggregate information; predictability; forecast model selection; VAR; factor models;

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References

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  1. Van Garderen, K. J. & Lee, K. & Pesaran M., 1998. "Cross-sectional Aggregation of Non-linear Models," Cambridge Working Papers in Economics 9803, Faculty of Economics, University of Cambridge.
  2. David F. Hendry, 2004. "Unpredictability and the Foundations of Economic Forecasting," Econometric Society 2004 Australasian Meetings 27, Econometric Society.
  3. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers.
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  7. Stock, James H, 1996. "VAR, Error Correction and Pretest Forecasts at Long Horizons," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(4), pages 685-701, November.
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  17. David Hendry & Michael P. Clements, 2001. "Pooling of Forecasts," Economics Papers 2002-W9, Economics Group, Nuffield College, University of Oxford.
  18. 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.
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Citations

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Cited by:
  1. Konstantin A. Kholodilin & Boriss Siliverstovs, 2005. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Discussion Papers of DIW Berlin 522, DIW Berlin, German Institute for Economic Research.
  2. Helmut Lütkepohl, 2010. "Forecasting Aggregated Time Series Variables: A Survey," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing,CIRET, vol. 2010(2), pages 1-26.
  3. Kirstin Hubrich & Kenneth D. West, 2010. "Forecast evaluation of small nested model sets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 574-594.
  4. Colin Bermingham & Antonello D’Agostino, 2014. "Understanding and forecasting aggregate and disaggregate price dynamics," Empirical Economics, Springer, vol. 46(2), pages 765-788, March.
  5. Juan de Dios Tena & Antoni Espasa & Gabriel Pino, 2008. "Forecasting Spanish inflation using information from different sectors and geographical areas," Statistics and Econometrics Working Papers ws080101, Universidad Carlos III, Departamento de Estadística y Econometría.
  6. 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.
  7. Guillermo Carlomagnol & Antoni Espasa, 2014. "The pairwise approach to model a large set of disaggregates with common trends," Statistics and Econometrics Working Papers ws141309, Universidad Carlos III, Departamento de Estadística y Econometría.
  8. Giacomo Sbrana, 2007. "Testing for Model Selection in Predicting Aggregate Variables," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 66(1), pages 3-28, March.
  9. Michael P. Clements & David F. Hendry, 2005. "Guest Editors' Introduction: Information in Economic Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 713-753, December.

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