Beschreibung:
In this paper we augment the Bayesian unobserved components model of the EU Commission to estimate the cyclical component of total factor productivity (TFP gap) with a factor structure to include a wide array of business cycle indicators. We demonstrate that this model extension considerably stabilizes the estimate of the of the TFP gap. Specifically, consider the usual autumn forecast of the EU Commission in October of a year T. For the last two "in-sample" years T − 2 and T − 1, and for the "now-cast" year T, the year-to-year revisions can be reduced by up to 30 percent. Improvements for the two "out-of-sample" years T + 1 and T + 2 also considered relevant by the EU Commission are quantitatively smaller (up to 10 percent) but still relevant. The results do vary across countries but are qualitatively robust with respect to different indicator sets, model specifications or vintages considered.