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The Assessement Of Uncertainty In Predictions Determined By The Variables Aggregation

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  • Mihaela Bratu

    (Academy of Economic Studies Bucharest)

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    Abstract

    The aggregation of the variables that compose an indicator, as GDP, which should beforecasted, is not mentioned explicitly in literature as a source of forecasts uncertainty. In thisarticle we demonstrate that variables aggregation is an important source of uncertainty inforecasting and we evaluate the accuracy of predictions for a variable obtained by aggregationusing two different strategies. Actually, the accuracy is an important dimension of uncertainty. Inthis study based on data on U.S. GDP and its components in 1995-2010, we found that GDP one-step-ahead forecasts made by aggregating the components with variable weights, modeled usingARMA procedure, have a higher accuracy than those with constant weights or the direct forecasts.Excepting the GDP forecasts obtained directly from the model, the one-step-ahead forecastsresulted form the GDP components‘ forecasts aggregation are better than those made on anhorizon of 3 years . The evaluation of this source of uncertainty should be considered formacroeconomic aggregates in order to choose the most accurate forecast.

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    Bibliographic Info

    Article provided by Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia in its journal Annales Universitatis Apulensis Series Oeconomica.

    Volume (Year): 2 (2011)
    Issue (Month): 13 ()
    Pages: 31

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    Handle: RePEc:alu:journl:v:2:y:2011:i:13:p:31

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    Related research

    Keywords: source of uncertainty; forecasts; accuracy; disaggregation over variables; strategy of prediction; DM test;

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    1. 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.
    2. 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.
    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. George Athanasopoulos & Farshid Vahid, 2006. "A Complete VARMA Modelling Methodology Based on Scalar Components," Monash Econometrics and Business Statistics Working Papers 2/06, Monash University, Department of Econometrics and Business Statistics.
    5. Raffaella Giacomini & Halbert White, 2003. "Tests of Conditional Predictive Ability," Econometrics 0308001, EconWPA.
    6. Marco Vega, 2004. "Policy Makers Priors and Inflation Density Forecasts," Econometrics 0403005, EconWPA.
    7. Hendry, David F. & Hubrich, Kirstin, 2006. "Forecasting economic aggregates by disaggregates," Working Paper Series 0589, European Central Bank.
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