Abstract
AbstractIn this paper we extend the Bayesian unobserved components model of the EU Commission to estimate the cyclical component of total factor productivity (TFP gap) with a factor structure that includes a wide array of business cycle indicators. We demonstrate that this model extension considerably stabilizes the estimate of the TFP gap for the largest five EMU countries. Specifically, consider the usual autumn forecast of the EU Commission in October of a year: Using the model extension, we reduce the year-to-year revisions in the TFP gap estimates of the current year and the two previous years by up to 30 percent. Revision reductions for the two years ahead also considered 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, and vintages considered.
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