Anticipating business-cycle turning points in real time using density forecasts from a VAR (with Natalia Soldatenkova)

Published in the "Journal of Macroeconomics", 2016, vol. 47, Part B, March, pp. 166-187. DOI information: 10.1016/j.jmacro.2015.12.002

Keywords: Density forecasts, Business-cycle turning points, Real-time data, Nowcasting, Bootstrap

Download: manuscript version November 2015 (PDF).

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.

(Latest update: October 2017)