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Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate

  • Hendry, David F.
  • Hubrich, Kirstin

To forecast an aggregate, we propose adding disaggregate variables, instead of combining forecasts of those disaggregates or forecasting by a univariate aggregate model. New analytical results show the effects of changing coefficients, mis-specification, estimation uncertainty and mis-measurement error. Forecastorigin shifts in parameters affect absolute, but not relative, forecast accuracies; mis-specification and estimation uncertainty induce forecast-error differences, which variable-selection procedures or dimension reductions can mitigate. In Monte Carlo simulations, different stochastic structures and interdependencies between disaggregates imply that including disaggregate information in the aggregate model improves forecast accuracy. Our theoretical predictions and simulations are corroborated when forecasting aggregate US inflation pre- and post 1984 using disaggregate sectoral data. JEL Classification: C51, C53, E31

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Paper provided by European Central Bank in its series Working Paper Series with number 1155.

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Date of creation: Feb 2010
Date of revision:
Handle: RePEc:ecb:ecbwps:20101155
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  8. Hubrich, Kirstin, 2003. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," Working Paper Series 0247, European Central Bank.
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  29. Rubén Hernández-Murillo & Michael T. Owyang, 2004. "The information content of regional employment data for forecasting aggregate conditions," Working Papers 2004-005, Federal Reserve Bank of St. Louis.
  30. 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.
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