Combined forecasts to improve Survey of Profession Forecasters predictions for quarterly inflation in the U.S.A

The main aim of this study is to evaluate and improve the Survey of Professional Forecasters (S.P.F.) quarterly inflation rate forecasts. According to the Diebold–Mariano test, on the horizon 1991:Q1–2015:Q1, there were no significant differences in accuracy between the four types of predictions provided by SPF (mean forecasts, median predictions, predictions of financial service providers [f1] and predictions of non-financial service providers [f2]). The main contribution is given by the use of the algorithm for stochastic search variable selection in order to construct Bayesian combined predictions. Considering the horizon 2013:Q1–2015:Q1, the proposed Bayesian combined predictions for rate of change in the quarterly average headline consumer price index (C.P.I.) level outperformed the initial experts’ expectations. The combined predictions based on the Bayesian approach and principal component analysis for core inflation and personal consumption expenditures inflation improved the accuracy of S.P.F. predictions and naïve forecasts on the horizon 2015:Q1–2016:Q1.