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Vulnerable growth: Bayesian GDP-at-Risk

Milan Szabo, Zlatuse Komarkova () and Martin Casta

from Czech National Bank

Abstract: This thematic article presents a new approach used by the CNB to try to quantify vulnerable real GDP growth risk. This risk is closely linked with the accumulated financial vulnerability in the economy, as the latter tends to deepen the trough of the economic cycle in the event of a sudden negative shock. The CNB's new approach uses Bayesian quantile regression to estimate the distribution of future real GDP growth. The estimate is conditional on the vulnerability of the financial system. The chosen approach makes it possible to link the distribution of real GDP growth for the Czech Republic to the growth forecast obtained from the CNB's official "g3+" structural model. This allows the CNB to obtain both more robust estimates of the distribution and a result coherent with its official forecast, despite the availability of only short time series. Quantifying vulnerable real GDP growth risk gives the CNB an additional financial stability indicator revealing the degree of vulnerability of the Czech financial system.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:cnb:ocpubc:tafs2020/2

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