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Forecasting an Aggregate in the Presence of Structural Breaks in the Disaggregates

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  • William Larson

    (Federal Housing Finance Agency)

Abstract

There is a debate in the literature on the best method to forecast an aggregate: (1) forecast the aggregate directly, (2) forecast the disaggregates and then aggregate, or (3) forecast the aggregate using disaggregate information. This paper contributes to this debate by suggesting that in the presence of moderate-sized structural breaks in the disaggregates, approach (2) is preferred because of the low power to detect mean shifts in the disaggregates using models of aggregates. In support of this approach are two exercises. First, a simple Monte Carlo study demonstrates theoretical forecasting improvements. Second, empirical evidence is given using pseudo-ex ante forecasts of aggregate proven oil reserves in the United States.

Suggested Citation

  • William Larson, 2015. "Forecasting an Aggregate in the Presence of Structural Breaks in the Disaggregates," Working Papers 2015-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
  • Handle: RePEc:gwc:wpaper:2015-002
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    File URL: https://www2.gwu.edu/~forcpgm/2015-002.pdf
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    References listed on IDEAS

    as
    1. Clements, Michael P. & Hendry, David F., 2006. "Forecasting with Breaks," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 12, pages 605-657, Elsevier.
    2. David F. Hendry & Kirstin Hubrich, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 216-227, April.
    3. David F. Hendry & Carlos Santos, 2005. "Regression Models with Data‐based Indicator Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(5), pages 571-595, October.
    4. Lutkepohl, Helmut, 2006. "Forecasting with VARMA Models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 6, pages 287-325, Elsevier.
    5. Jennifer Castle & David Hendry & Nicholas W.P. Fawcett, 2011. "Forecasting breaks and forecasting during breaks," Economics Series Working Papers 535, University of Oxford, Department of Economics.
    6. Castle, Jennifer L. & Fawcett, Nicholas W.P. & Hendry, David F., 2009. "Nowcasting is not Just Contemporaneous Forecasting," National Institute Economic Review, Cambridge University Press, vol. 210, pages 71-89, October.
    7. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, vol. 169(2), pages 239-246.
    8. Santos, Carlos, 2008. "Impulse saturation break tests," Economics Letters, Elsevier, vol. 98(2), pages 136-143, February.
    9. Farzin, Y. H., 2001. "The impact of oil price on additions to US proven reserves," Resource and Energy Economics, Elsevier, vol. 23(3), pages 271-292, July.
    10. Jennifer L. Castle & David F. Hendry, 2010. "Nowcasting from disaggregates in the face of location shifts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 200-214.
    11. Thomas Steichen, 2000. "NCF: Stata modules related to the noncentral F distribution," Statistical Software Components S411902, Boston College Department of Economics.
    12. Carlos Santos & David Hendry & Soren Johansen, 2008. "Automatic selection of indicators in a fully saturated regression," Computational Statistics, Springer, vol. 23(2), pages 317-335, April.
    13. Clements, Michael P & Hendry, David F, 1996. "Intercept Corrections and Structural Change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 475-494, Sept.-Oct.
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    Cited by:

    1. Larson, William D. & Sinclair, Tara M., 2022. "Nowcasting unemployment insurance claims in the time of COVID-19," International Journal of Forecasting, Elsevier, vol. 38(2), pages 635-647.

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    More about this item

    Keywords

    Model selection; Intercept correction; Forecast robustification;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q3 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation

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