Addressing COVID-19 Outliers in BVARs with Stochastic Volatility

We review recent advances in dynamic stochastic general equilibrium theory concerned with the emergence of fat‐tailed time‐series distributions. Focusing on mechanisms that are firmly grounded in structural equilibrium models, we provide a common reference framework to organize existing contributions according to whether they entail extreme business cycle swings as an endogenous response to small and short‐lived shocks (“thin in, fat out”), or rather as an automatic consequence of large and/or heteroskedastic exogenous impulses (“fat in, fat out”). Within the former class, non‐Gaussian features of equilibrium patterns can endogenously emerge in fully rational, Gaussian environments. Using an empirically plausible real business cycle framework, we also report novel simulation‐based evidence that helps reconcile theoretical predictions with the documented higher‐order properties of time‐series data for output measures.

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Addressing COVID-19 Outliers in BVARs with Stochastic Volatility

Cited by 2 publications

References 30 publications

Precision-based sampling for state space models that have no measurement error

Precision-based sampling for state space models that have no measurement error

Fat‐tailed DSGE models: A survey and new results

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