Anticipating business-cycle turning points in real time using density forecasts from a VAR

• Real-time density forecasts from linear VARs are proposed to deal with the determination of turning points.
• The dimensionality of the systems is reduced with bootstrapped subset (zero) restrictions.
• Method can be applied to monthly data where a six-month growth rate of industrial production is a good indicator of the recessionary stance.
• Out-of-sample performance evaluations show very competitive results.
• The method works well especially in the case of the USA, for Germany the anticipation of a recession based on industrial production dynamics is more difficult.

For the timely detection of business-cycle turning points we suggest to use medium-sized linear systems (subset VARs with automated zero restrictions) to forecast monthly industrial production index publications one to several steps ahead, and to derive the probability of the turning point from the bootstrapped forecast density as the probability mass below (or above) a suitable threshold value. We show how this approach can be used in real time in the presence of data publication lags and how it can capture the part of the data revision process that is systematic. Out-of-sample evaluation exercises show that the method is competitive especially in the case of the US, while turning-point forecasts are in general more difficult in Germany.