Rare Shocks vs. Non-linearities: What Drives Extreme Events in the Economy? Some Empirical Evidence
Working Papers from Czech National Bank
A small-scale vector autoregression (VAR) is used to shed some light on the roles of extreme shocks and non-linearities during stress events observed in the economy. The model focuses on the link between credit/financial markets and the real economy and is estimated on US quarterly data for the period 1984â€“2013. Extreme shocks are accounted for by assuming t-distributed reduced-form shocks. Non-linearity is allowed by the possibility of regime switch in the shock propagation mechanism. Strong evidence for fat tails in error distributions is found. Moreover, the results suggest that accounting for extreme shocks rather than explicit modeling of non-linearity contributes to the explanatory power of the model. Finally, it is shown that the accuracy of density forecasts improves if non-linearities and shock distributions with fat tails are considered.
Keywords: Bayesian VAR; density forecasting; fat tails; non-linearity (search for similar items in EconPapers)
JEL-codes: C11 C32 E44 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-fdg, nep-for, nep-mac and nep-rmg
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Journal Article: Rare shocks vs. non-linearities: What drives extreme events in the economy? Some empirical evidence (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:cnb:wpaper:2015/04
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