THE ASSESSEMENT OF UNCERTAINTY IN PREDICTIONS DETERMINED BY THE VARIABLES AGGREGATION

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

The Uncertainty of USA GDP Forecasts Determined by the Variables Aggregation

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 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.&nbsp

The Prediction of Inflation in Romania in Uncertainty Conditions

Based on data of inflation forecasts provided quarterly by the National Bank of Romania, forecast intervals were built using the method of historical forecast errors. Forecast intervals were built considering that the forecast error series is normally distributed of zero mean and standard deviation equal to the RMSE (root mean squared error) corresponding to historical forecast errors.  We introduced as a measure of economic state the indicator– relative variance of the phenomenon at a specific time in relation with the variance on the entire time horizon. For Romania, when inflation rates follows an AR (1), we have improved the technique of building forecast intervals taking into account the state of the economy in each period for which data were recorded. We consider really necessary the building of forecasts intervals, in order to have a measure of predictions uncertainty

SMANJENJE NEODLUČNOSTI PRI DONOŠENJU ODLUKA VREDNOVANJEM UČINKA MAKROEKONOMSKIH PREDVIĐANJA U RUMUNJSKOJ

The evaluation of macroeocnomic forecasts performance does not include only the calculating of some statistical measures, rather controversial in literature, like root mean squares error or absolute mean error. In theory and economic practice, three directions have been traced regarding the evaluation of forecasts performance: the analyse of accuracy, bias and efficiency. Using the forecasted values on medium run of inflation rate and unemplyment rate through the period from 2004-2010 in Romania, we get a better degree of accuracy and a lower efficiency for forecasts made by National Commission of Forecasting comparing to those based of Dobrescu model used by Institute of Economic Forecasting. Following the international tendency, the forecasts are, in all cases, biased because of difficulties in precise anticipation of shocks which affect the economy. Forecasts performance is indestructible related by their uncertainty, RMSE, the measure of evaluating the accuracy being used in building forecast intervals based on historical errors. For forecasted values of inflation rate published by National Bank of Romania we propose a new way of building forecast interval in order to take into account the economic shocks.Vrednovanje performansi makroekonomskih predviđanja ne uključuje samo izračun nekoliko statističkih mjera, prilično kontroverznih u literaturi, kao što su korijen srednje kvadratne greške ili srednje apsolutne greške. U teoriji i ekonomskoj praksi, možemo pratiti tri smjera u vezi s vrednovanjem performansi predviđanja: analizu točnosti, pristranosti i efikasnosti. Koristeći prognozirane srednje vrijednosti stope inflacije i stope nezaposlenosti u periodu od 2004. do 2010. u Rumunjskoj, dobivamo bolji stupanj točnosti i manju efikasnost za prognoze koje daje Nacionalna komisija za prognoziranje u usporedbi s onima baziranim na Dobrescu modelu kojeg koristi Institut za ekonomska prognoziranja. Slijedeći međunarodni trend, predviđanja su u svakom slučaju pristrana radi poteškoća u preciznom predviđanju šokova koji utječu na ekonomiju. Performansa predviđanja je neraskidivo vezana za njihovu nesigurnost, RMSE, mjeru vrednovanja točnosti koju koristimo u stvaranju prognostičkih intervala zasnovanih na povijesnim pogreškama. Za predložene vrijednosti stope inflacije koje objavljuje Rumunjska Narodna Banka, predlažemo nov način kreiranja prognostičkog intervala kako bi se uzeli u obzir ekonomski šokovi

THE ACCURACY OF UNEMPLOYMENT RATE FORECASTS IN ROMANIA AND THE ACTUAL ECONOMIC CRISIS

In this study, the problem of forecasts accuracy is analysed on three different forecasting horizons: during the actual economic crisis, in few years before the crisis and on a large horizon. The accuracy of the forecasts made by European Commission, National Commission for Prognosis (NCP) and Institute for Economic Forecasting (IEF) for unemployment rate in Romania is assessed. The most accurate predictions on the forecasting horizons 2001-2011 and 2009-2011 were provided by IEF and the less accurate by NCP. These results were gotten using U1 Theil’s statistic and a new method that has not been used before in literature in this context. The multi-criteria ranking was applied to make a hierarchy of the institutions regarding the accuracy and five important accuracy measures were taken into account at the same time: mean errors, mean squared error, root mean squared error, U1 and U2 statistics of Theil. In few years before crisis (2006-2008) another hierarchy of institutions were gotten using the accuracy criterion: NCP, IEF and EC. The combined forecasts of institutions’ predictions are the best strategy to improve the forecasts accuracy on overall and before the crisis. During the economic crisis IEF provided the most accurate predictions, the combined forecasts being a good strategy of improving only the forecasts made by NCP and EC using inversely MSE scheme and equally weighted scheme. The assessment and improvement of forecasts accuracy have an important contribution in growing the quality of decisional process

IMPROVEMEnTS In ASSESSInG THE FORECASTS ACCURACY - A CASE STUDY FOR ROMAnIAn MACROECOnOMIC FORECASTS

The objective of this study is to introduce new forecasts’ accuracy measures for two types of predictions: point forecasts (radical of order n of the mean of squared errors, mean for the differencebetween each predicted value and the mean of the effective values, ratio of radicals of sum of squared errors (RRSSE- for forecasts comparisons), different versions of U2 Theil’s statistic)) and forforecast intervals (number of intervals including the realization, difference between the realization and the lower limit, the upper one, respectively the interval centre). Comparisons are made to presentthe differences in results determined by the application of the classical measures of predictions accuracy for the inflation and unemployment rate forecasts provided for Romania by Institute forEconomic Forecasting (IEF) and National Commission of Prognosis (NCP) on the horizon 2010- 2012 and the values of new point forecasts accuracy measures. The hierarchy of predictions provided by the classical indicators and by the new ones are different. A novelty in literature is also brought by the methods of building the forecasts intervals. In addition to the classical interval basedon historical error method, some new techniques of building forecasts are used: intervals based on the standard deviation and those constructed using bootstrap technique bias-corrected-accelerated(BCA) bootstrap method

How to Improve the SPF Forecasts?

The reduction of forecasts uncertainty is one of the major goal to be achieved in forecasting process. This implies the improvement of predictions accuracy. In this study, many types of forecasts of the annual rate of change for the HICP for EU were developed, their accuracy was evaluated and compared with the accuracy of SPF predictions. All the proposed predictions for January 2010-May 2012 (those based on a random walk developed for 1997-2009, combined forecasts, the median and the mean of forecasts, predictions based on different econometric models that take into account the previous SPF forecasts) were not more accurate than the naïve forecasts or SPF ones. A considerably improvement of the accuracy was gotten for predictions based on mean error of SPF expectations for 1997-2009 and the previous registered value. This empirical strategy of building more accurate forecasts was better than the classical theoretical approaches from literature, but it is still less accurate than the naïve forecasts that could be made for UE inflation rate. So, the forecasts based on a simple econometric model as the random walk from the naïve approach are the most accurate, conclusion that is in accordance with the latest researches in literature and with one of the essential condition in forecasting theory

Two quantitative forecasting methods for macroeconomic indicators in Czech Republic

Econometric modelling and exponential smoothing techniques are two quantitative forecasting methods with good results in practice, but the objective of the research was to find out which of the two techniques are better for short run predictions. Therefore, for inflation, unemployment and interest rate in Czech Republic some accuracy indicators were calculated for the predictions based on these methods. Short run forecasts on a horizon of 3 months were made for December 2011-February 2012, the econometric models being updated. For Czech Republic, the exponential smoothing techniques provided more accurate forecasts than the econometric models (VAR(2) models, ARMA procedure and models with lagged variables). One explication for the better performance of smoothing techniques would be that in the chosen countries the short run predictions more influenced by the recent evolution of the indicators.

Using the econometric approach to improve the accuracy of GDP deflator forecasts

In this article, the GDP deflator is predicted starting from econometric models of historical errors of forecasts based on Dobrescu macromodel. In Romania, a significant relationship between GDP deflator and GDP index predictions was not confirmed. However, there is an important dependence between the forecasts errors of the two variables. Econometric models were built for real errors, absolute ones and squared errors of Dobrescu predictions of 1997-2008. The forecasts errors of GDP deflator for 2009, 2010 and 2011 are lower in all cases than those based on Dobrescu macroeconometric model, the accuracy indicators being a proof of this. But, only the forecasts based on absolute errors are superior to naïve forecasts. This econometric approach for historical forecasts errors are a very good strategy of improving the experts predictions