Vector autoregression models with skewness and heavy tails

Karlsson, Sune,Professor,1960-Örebro universitet,Handelshögskolan vid Örebro Universitet,Unit of Statistics,Örebro University, Sweden (author)

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 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.

Mazur, Stepan,1988-Linnéuniversitetet,Örebro universitet,Handelshögskolan vid Örebro Universitet,Unit of Statistics, School of Business, Örebro University, Sweden; Department of Economics and Statistics, School of Business and Economics, Linnaeus University, Sweden,Institutionen för nationalekonomi och statistik (NS),DISA;DSM(Swepub:lnu)stmaac (author)
Nguyen, Hoang,1989-Linköpings universitet,Örebro universitet,Handelshögskolan vid Örebro Universitet,Unit of Statistics,Örebro University, Sweden,Produktionsekonomi(Swepub:liu)hoang65 (author)
Örebro universitetHandelshögskolan vid Örebro Universitet (creator_code:org_t)

By the author/editor: Karlsson, Sune, ...; Mazur, Stepan, 1 ...; Nguyen, Hoang, 1 ...

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Mathematics; and Probability Theo ...

By the university: Örebro University; Linnaeus University; Linköping University

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.