The Assessement Of Uncertainty In Predictions Determined By The Variables Aggregation
AbstractThe 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 InfoArticle 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 ()
Contact details of provider:
source of uncertainty; forecasts; accuracy; disaggregation over variables; strategy of prediction; DM test;
Find related papers by JEL classification:
- E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
- E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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