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Forecasting Contemporaneous Aggregates with Stochastic Aggregation Weights

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  • Ralf Brueggemann
  • Helmut Luetkepohl

Abstract

Many contemporaneously aggregated variables have stochasticaggregation 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 disaggregate components, a forecast which aggregates a multivariate forecast of the disaggregate components and the aggregation weights, and a forecast which aggregates univariate forecasts for individual disaggregate components and the aggregation weights. In empirical illustrations based on aggregate GDP and money growth rates, we find forecast efficiency gains from using the information in the stochastic aggregation weights. A Monte Carlo study confirms that using the information on stochastic aggregation weights explicitly may result in forecast mean squared error reductions.

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Bibliographic Info

Paper provided by European University Institute in its series Economics Working Papers with number ECO2011/17.

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Date of creation: 2011
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Handle: RePEc:eui:euiwps:eco2011/17

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Keywords: Aggregation; autoregressive process; mean squared error;

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  1. Beyer, A. & Doornik, J.A. & Hendry, D.F., 2000. "Constructing Historical Euro-Zone Data," Economics Working Papers eco2000/10, European University Institute.
  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. 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.
  4. Lutkepohl, Helmut, 1984. "Linear transformations of vector ARMA processes," Journal of Econometrics, Elsevier, vol. 26(3), pages 283-293, December.
  5. Kirstin Hubrich, 2003. "Forecasting euro area inflation: does aggregating forecasts by HICP component improve forecast accuracy?," Working Paper Series 247, European Central Bank.
  6. 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.
  7. Tiao, G. C. & Guttman, Irwin, 1980. "Forecasting contemporal aggregates of multiple time series," Journal of Econometrics, Elsevier, vol. 12(2), pages 219-230, February.
  8. 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.
  9. Lutkepohl, Helmut, 1984. "Forecasting Contemporaneously Aggregated Vector ARMA Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 201-14, July.
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