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Variables Aggregation-Source of Uncertainty in Forecasting

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  • Bratu Simionescu Mihaela

    (Faculty of Cybernetics, Statistics and Economic Informatics Academy of Economic Studies Bucharest, Romania)

Abstract

The GDP forecasting presents a particularity resulted from the fact that this macroeconomic indicator can be analyzed in its quality of aggregate. Therefore, the GDP can be predicted directly using an econometric model with lagged variables represented by the aggregate component. On the other hand, the same GDP can be predicted by aggregating the forecasts of its components. The aim of this study is to find out which strategy generates the most accurate one-step-ahead prediction and if combined forecasts can be a solution of improving the forecasts accuracy. Starting from the GDP oneyear- ahead predictions made for 2009-2011 using the two strategies, measures of accuracy were calculated and the directly predicted GDP are better than those based on aggregating the components using constant and variable weights. Combined forecasts did not improve the accuracy of the predictions based on the mentioned strategies. This research is a good proof for putting the basis of considering the variables aggregation as an important source of uncertainty in forecasting.

Suggested Citation

  • Bratu Simionescu Mihaela, 2012. "Variables Aggregation-Source of Uncertainty in Forecasting," Scientific Annals of Economics and Business, Sciendo, vol. 59(2), pages 1-13, December.
    • Handle: RePEc:vrs:aicuec:v:59:y:2012:i:2:p:1-13:n:1
    DOI: 10.2478/v10316-012-0028-3
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    References listed on IDEAS

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    1. Hendry, David & Hubrich, Kirstin, 2006. "Forecasting Economic Aggregates by Disaggregates," CEPR Discussion Papers 5485, C.E.P.R. Discussion Papers.
    2. Clements, Michael P & Hendry, David F, 1995. "Forecasting in Cointegration Systems," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 127-146, April-Jun.
    3. 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.
    4. Debby Lanser & Henk Kranendonk, 2008. "Investigating uncertainty in macroeconomic forecasts by stochastic simulation," CPB Discussion Paper 112, CPB Netherlands Bureau for Economic Policy Analysis.
    5. Neil R. Ericsson, 2001. "Forecast uncertainty in economic modeling," International Finance Discussion Papers 697, Board of Governors of the Federal Reserve System (U.S.).
    6. Marco Vega, 2004. "Policy Makers Priors and Inflation Density Forecasts," Econometrics 0403005, University Library of Munich, Germany.
    7. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    8. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.
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