Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/244582 
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
Working Paper No. 8/2021
Verlag: 
Örebro University School of Business, Örebro
Zusammenfassung: 
With uncertain changes of the economic environment, macroeconomic downturns during recessions and crises can hardly be explained by a Gaussian structural shock. There is evidence that the distribution of macroeconomic variables is skewed and heavy tailed. In this paper, we contribute to the literature by extending a vector autore- gression (VAR) model to account for a more realistic assumption of the multivariate distribution of the macroeconomic variables. We propose a general class of generalized hyperbolic skew Student's t distribution with stochastic volatility for the error term in the VAR model that allows us to take into account skewness and heavy tails. Tools for Bayesian inference and model selection using a Gibbs sampler are provided. In an empirical study, we present evidence of skewness and heavy tails for monthly macroe- conomic variables. The analysis also gives a clear message that skewness should be taken into account for better predictions during recessions and crises.
Schlagwörter: 
Vector autoregression
Skewness and heavy tails
Generalized hyper- bolic skew Students t distribution
Stochastic volatility
Markov Chain Monte Carlo
JEL: 
C11
C15
C16
C32
C52
Dokumentart: 
Working Paper

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