Anticipating business-cycle turning points in real time using density forecasts from a VAR |
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| Institution: | 1. Macroeconomic Policy Institute (IMK), Hans-Böckler-Str. 39, 40476 Düsseldorf, Germany;2. Free University of Berlin, Institute of Statistics and Econometrics, Germany;3. University of Bonn, Bonn Graduate School of Economics (BGSE), Lennéstraße 35, 53113 Bonn, Germany;1. Robert R. and Katheryn A. Dockson Chair in Economics and International Relations, USC and the NBER, United States\n;2. EQC-MPI Chair in the Economics of Disasters, Victoria University of Wellington, New Zealand;1. Kings College, University of London, United Kingdom;2. George Mason University, 3351 Fairfax Dr., MS 3B1 Arlington, VA 22201, USA;3. “Sapienza” University of Rome, via castro laurenziano, 9 - 00161, Rome, Italy |
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| Abstract: | 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. |
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