Modelling Okun's law - does non-Gaussianity matter? - Tamás Kiss; Hoang Nguyen; Pär Österholm (Örebro University School of Business, 2022)
In this paper, we analyse Okun's law - a relation between the change in the unemployment rate and GDP growth - using data from Australia, the euro area, the United Kingdom and the United States. More specifically, we assess the relevance of non-Gaussianity when modelling the relation. This is done in a Bayesian VAR framework with stochastic volatility where we allow the different models' error distributions to have heavier-than-Gaussian tails and skewness. Our results indicate that accounting for heavy tails yields improvements over a Gaussian specification in some cases, whereas skewness appears less fruitful. In terms of dynamic effects, a shock to GDP growth has robustly negative effects on the change in the unemployment rate in all four economies.
Keywords: C11; C32; C52; E32; 330; Bayesian VAR; Heavy tails; GDP growth; Unemployment