Vector autoregression models with skewness and heavy tails

Sune Karlsson (), Stepan Mazur and Hoang Nguyen

Journal of Economic Dynamics and Control, 2023, vol. 146, issue C

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 autoregression (VAR) model to account for more realistic assumptions on the multivariate distribution of macroeconomic variables. We propose a general class of generalized hyperbolic skew Student’s t distribution with stochastic volatility for the innovations in the VAR model that allows us to take into account both 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 macroeconomic variables. The analysis also gives a clear message that skewness is a value-added feature to VAR models with heavy tails.

Keywords: Vector autoregression; Skewness and heavy tails; Generalized hyperbolic skew Student’s 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)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (5)

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Working Paper: Vector autoregression models with skewness and heavy tails (2021)
Working Paper: Vector autoregression models with skewness and heavy tails (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:146:y:2023:i:c:s0165188922002834

DOI: 10.1016/j.jedc.2022.104580

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