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Does a meta-combining method lead to more accurate forecasts in the decision-making process?

100%

GULAY E. , ARAS S.

nr 3

101-124

To improve forecasting accuracy, researchers employed various combination techniques for a long time. When researchers deal with time series data by using dissimilar models, the combined forecasts of these models are expected to be superior. Deriving a weighting scheme performing better than simple but hard−to−beat combining methods has always been challenging. In this study, a new weighting method based on the hybridisation of combining algorithms is proposed. Five popular datasets were utilised to demonstrate the effectiveness of the proposed method in an out-of-sample context. The results indicate that the proposed method leads to more accurate forecasts than other combining techniques used in the study.

The Performance of Unemployment Rate Predictions in Romania. Strategies to Improve the Forecasts Accuracy

75%

Simionescu M.

tom 13

nr 4

161-175

The evaluation and improvement of forecasts accuracy generate growth in the quality of decisional process. In Romania, the most accurate predictions for the unemployment rate on the forecasting horizon 2001-2012 were provided by the Institute for Economic Forecasting (IEF) that is followed by European Commission and National Commission for Prognosis (NCP). The result is based on U1, but if more indicators are taken into consideration at the same time using the multi-criteria ranking, the conclusion remains the same. A suitable strategy for improving the degree of accuracy for these forecasts is represented by the combined forecasts. The accuracy of NCP predictions can be improved on the horizon 2001-2012, if the initial values are smoothed using Holt-Winters technique and Hodrick-Prescott filter. The use of Monte Carlo method to simulate the forecasted unemployment rate proved to be the best way to improve the predictions accuracy. Starting from an AR(1) model for the interest variable, the uncertainty analysis was included, the simulations being made for the parameters. Actually, the means of the forecasts distributions for unemployment are considered as point predictions which outperform the expectations of the three institutions. The strategy based on Monte Carlo method is an original contribution of the author introduced in this article regarding the empirical strategies of getting better predictions.

Point forecasts based on the limits of the forecast intervals to improve the SPF predictions

75%

(Simionescu) Mihaela B.

nr 2

1-11

In this study, some strategies of improving the forecasts accuracy were tested for the USA quarterly inflation rate. The classical filters and Holt Winters technique were applied for one-step-ahead forecasts on a horizon of four quarters from 1975 to 2011. Combined forecasts were made using the original SPF values and the new predictions based on filters and Holt Winters method. Some conclusions are valid for all the years for which forecasts are provided: combined predictions based on classical schemes (optimal, inverse weighted and equally weighted scheme) and the smoothed SPF forecasts using Holt Winters technique are two strategies of improving the accuracy of SPF expectations. However, the last one is the best, one reason being that the future evolution of inflation in USA is determined by recent values.