Predicting U.S. recessions through a combination of probability forecasts

Recently De Luca and Carfora (Statistica e Applicazioni 8:123–134, <CitationRef CitationID="CR5">2010</CitationRef>) have proposed a novel model for binary time series, the Binomial Heterogenous Autoregressive (BHAR) model, successfully applied for the analysis of the quarterly binary time series of U.S. recessions. In this work we want to measure the efficacy of the out-of-sample forecasts of the BHAR model compared to the probit models by Kauppi and Saikkonen (Rev Econ Stat 90:777–791, <CitationRef CitationID="CR13">2008</CitationRef>). Given the substantial indifference of the predictive accuracy between the BHAR and the probit models, a combination of forecasts using the method proposed by Bates and Granger (Oper Res Q 20:451–468, <CitationRef CitationID="CR1">1969</CitationRef>) for probability forecasts is analyzed. We show how the forecasts obtained by the combination between the BHAR model and each of the probit models are superior compared to the forecasts obtained by each single model. Copyright Springer-Verlag Berlin Heidelberg 2014