Combination schemes for turning point predictions

We propose new forecast combination schemes for predicting turning points of business cycles. The proposed combination schemes are based on the forecasting performances of a given set of models with the aim to provide better turning point predictions. In particular, we consider predictions generated by autoregressive (AR) and Markov-switching AR models, which are commonly used for business cycle analysis. In order to account for parameter uncertainty we consider a Bayesian approach for both estimation and prediction and compare, in terms of statistical accuracy, the individual models and the combined turning point predictions for the United States and the Euro area business cycles.

► This paper proposes to use Bayesian inference to combining turning point forecasts from linear and non-linear models. ► The first methodology combines the forecasts from the models and then detects the turning points. ► The second methodology detects the turning points from the models and then combines them. ► We find that the forecast abilities of the two strategies are cycle-specific and need to be evaluated in the problem at hand.