Vector autoregression models with skewness and heavy tails

Sune Karlsson (), Stepan Mazur and Hoang Nguyen

No 2021:8, Working Papers from Örebro University, School of Business

Abstract: 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.

Keywords: Vector autoregression; Skewness and heavy tails; Generalized hyper- bolic skew Students t distribution; Stochastic volatility; Markov Chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 C15 C16 C32 C52 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2021-05-20
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mac, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Journal Article: Vector autoregression models with skewness and heavy tails (2023)
Working Paper: Vector autoregression models with skewness and heavy tails (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:oruesi:2021_008

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