Tamas Kiss (), Hoang Nguyen () and Pär Österholm ()
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Tamas Kiss: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden
Hoang Nguyen: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden
Pär Österholm: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden
Abstract: 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 mod-elling 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: Bayesian VAR; Heavy tails; GDP growth; Unemployment
Language: English
25 pages, January 17, 2022
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