New Methods of Evaluating the Forecasts Accuracy: A Case Study for USA Inflation

Using the two-year inflation forecasts provided by CBO, Blue Chips and Administration for
USA on the forecasting horizon 1982-2011, the accuracy of forecasts was assessed.
According to U1 Theil’s statistic, the CBO projections are the best, followed by
Administration and Blue Chips predictions. A new accuracy measure is proposed to be
introduce in literature (ratio of radicals of sum of squared errors) in order to compare the
forecasts with the naïve ones. The same hierarchy of institutions, according to accuracy
criterion is obtained if all the computed accuracy indicators are taking into account using
ranks method. According to the relative distance method with respect to the better institution
and other methods (binary logistic regression and some non-parametric tests (Wilcoxon and
Kruskall-Wallis tests)) the following rank is gotten: Administration, CBO and Blue Chips. All
these methods (multi-criteria ranking, logistic regression and non-parametric tests) were not
mentioned before in literature as possible ways of comparing the forecasts accuracy, but most
of them gave better results than the classical U1, because these methods take into
consideration more aspects regarding the accuracy measurement. Some empirical strategies
of improving the forecasts accuracy were applied (combined forecasts, smoothed predicted
values based on Holt-Winters technique, Hodrick-Prescott, Baxter King and
Christiano-Fitzegerald filters), getting more accurate predictions only for the combined
forecasts of Blue Chips and Administration using inverse MSE scheme (the highest
improvement) and equally weighted scheme, but also for forecasts based on CBO and Blue
Chips using optimal scheme.