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Forecasting contemporaneous aggregates with stochastic aggregation weights

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  • Brüggemann, Ralf
  • Lütkepohl, Helmut

Abstract

Many contemporaneously aggregated variables have stochastic aggregation weights. We compare different forecasts for such variables, including univariate forecasts of the aggregate, a multivariate forecast of the aggregate that uses information from the disaggregated components, a forecast which aggregates a multivariate forecast of the disaggregate components and the aggregation weights, and a forecast which aggregates univariate forecasts of individual disaggregate components and the aggregation weights. In empirical illustrations based on aggregate GDP and money stock series, we find forecast mean squared error reductions when information in the stochastic aggregation weights is used.

Suggested Citation

  • Brüggemann, Ralf & Lütkepohl, Helmut, 2013. "Forecasting contemporaneous aggregates with stochastic aggregation weights," International Journal of Forecasting, Elsevier, vol. 29(1), pages 60-68.
  • Handle: RePEc:eee:intfor:v:29:y:2013:i:1:p:60-68
    DOI: 10.1016/j.ijforecast.2012.05.007
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    1. Beyer, Andreas & Doornik, Jurgen A & Hendry, David F, 2001. "Constructing Historical Euro-Zone Data," Economic Journal, Royal Economic Society, vol. 111(469), pages 102-121, February.
    2. 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.
    3. 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.
    4. Lutkepohl, Helmut, 1984. "Linear transformations of vector ARMA processes," Journal of Econometrics, Elsevier, vol. 26(3), pages 283-293, December.
    5. Helmut Lütkepohl, 2010. "Forecasting Aggregated Time Series Variables: A Survey," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(2), pages 1-26.
    6. Tiao, G. C. & Guttman, Irwin, 1980. "Forecasting contemporal aggregates of multiple time series," Journal of Econometrics, Elsevier, vol. 12(2), pages 219-230, February.
    7. Ansley, Craig F. & Spivey, W. Allen & Wrobleski, William J., 1977. "On the structure of moving average processes," Journal of Econometrics, Elsevier, vol. 6(1), pages 121-134, July.
    8. A. Espasa & E. Senra & R. Albacete, 2002. "Forecasting inflation in the European Monetary Union: A disaggregated approach by countries and by sectors," The European Journal of Finance, Taylor & Francis Journals, vol. 8(4), pages 402-421.
    9. Carson, Richard T. & Cenesizoglu, Tolga & Parker, Roger, 2011. "Forecasting (aggregate) demand for US commercial air travel," International Journal of Forecasting, Elsevier, vol. 27(3), pages 923-941, July.
    10. 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.
    11. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809.
    12. Lütkepohl Helmut, 2011. "Forecasting Nonlinear Aggregates and Aggregates with Time-varying Weights," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 107-133, February.
    13. 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.
    14. 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.
    15. Lutkepohl, Helmut, 1984. "Forecasting Contemporaneously Aggregated Vector ARMA Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 201-214, July.
    16. 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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    More about this item

    Keywords

    Aggregation; Autoregressive process; Mean squared error;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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