Vector autoregression models with skewness and heavy tails

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 8/2021

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Statistical Simulation Methods: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Model Evaluation, Validation, and Selection
• Subject: Vector autoregression ; Skewness and heavy tails ; Generalized hyper- bolic skew Students t distribution ; Stochastic volatility ; Markov Chain Monte Carlo

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Karlsson, Sune ; Mazur, Stepan ; Nguyen, Hoang
• Event: Veröffentlichung
• (who): Örebro University School of Business
• (where): Örebro
• (when): 2021

Further information

• Handle: http://hdl.handle.net/10419/244582
• Last update: 10.03.2025, 11:43 AM CET