THE ASSESSEMENT OF UNCERTAINTY IN PREDICTIONS
            DETERMINED BY THE VARIABLES AGGREGATION

                       Mihaela Bratu, Academy of Economic Studies Bucharest


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 one-
step-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.

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

JEL Classification: E21, E27,C51, C53