Working Papers,
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

JEL-codes: C11; C15; C16; C32; C52

37 pages, May 20, 2021

Full text files

wp-8-2021.pdf  Full text

Download statistics

• Örebro University, School of Business
• Home page for this series

Questions (including download problems) about the papers in this series should be directed to ()
Report other problems with accessing this service to Sune Karlsson ().

RePEc:hhs:oruesi:2021_008This page generated on 2024-09-13 22:16:32.