Title: The Assessement Of Uncertainty In Predictions Determined By The Variables Aggregation
Abstract: The aggregation of the variables that compose an indicator, as GDP, which should be forecasted, is not mentioned explicitly in literature as a source of forecasts uncertainty.In this article we demonstrate that variables aggregation is an important source of uncertainty in forecasting and we evaluate the accuracy of predictions for a variable obtained by aggregation using two different strategies.Actually, the accuracy is an important dimension of uncertainty.In this study based on data on U.S. GDP and its components in 1995-2010, we found that GDP onestep-ahead forecasts made by aggregating the components with variable weights, modeled using ARMA 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 forecasts resulted form the GDP components' forecasts aggregation are better than those made on an horizon of 3 years .The evaluation of this source of uncertainty should be considered for macroeconomic aggregates in order to choose the most accurate forecast.