Modelling Okun's law - does non-Gaussianity matter?

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Working Paper ; No. 1/2022

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Bayesian Analysis: 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 ; Business Fluctuations; Cycles
• Thema: Bayesian VAR ; Heavy tails ; GDP growth ; Unemployment

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Kiss, Tamás ; Nguyen, Hoang ; Österholm, Pär
• Ereignis: Veröffentlichung
• (wer): Örebro University School of Business
• (wo): Örebro
• (wann): 2022

Weitere Informationen

• Handle: http://hdl.handle.net/10419/262150
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ