The main purpose of this research is to show the diversity of statistical methods that could be used to assess and compare forecasts accuracy. Some of the statistical approaches were not used before in literature to evaluate the forecasts accuracy. The different methods applied to compare the accuracy of the USA inflation forecasts on the horizon 1976-2012 started from the predictions provided by Survey of Professional Forecasters (SPF), Congressional Budget Office (CBO), Blue Chips (BC), and Administration, determining different results. According to U1 Theil’s statistic, non-parametric tests and a new indicator proposed by us (RRSSE- ratio of radicals of sum of squared errors), the best forecasts were provided by Administration and the less accurate by SPF. The Spearman’s and Kendall’s coefficients of correlation and the ranks method gavea hierarchy of institutions performance regarding the accuracy that starts with BC and finished with SPF. The logistic regression computed by the author and the relative distance to the maximal performance method considered CBO as the best institution. Some methods of improving the forecasts accuracy were applied, getting more accurate predictions for the combined forecasts of BC and CBO using optimal scheme of combination. The smoothed predicted values based on Hodrick-Prescott filter outperformed all the initial predictions and the combined ones.

forecasts, accuracy, logistic regression, combined forecasts, non-parametric tests, filters, multi-criteria ranking