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

Sven Schreiber and Natalia Soldatenkova

Journal of Macroeconomics, 2016, vol. 47, issue PB, 166-187

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.

Keywords: Density forecasts; Business-cycle turning points; Real-time data; Nowcasting; Bootstrap (search for similar items in EconPapers)
JEL-codes: C53 E37 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Working Paper: Anticipating business-cycle turning points in real time using density forecasts from a VAR (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmacro:v:47:y:2016:i:pb:p:166-187

DOI: 10.1016/j.jmacro.2015.12.002

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