Arbeitspapier

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

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.

Sprache
Englisch

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

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

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

Handle
Letzte Aktualisierung
10.03.2025, 11:43 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Kiss, Tamás
  • Nguyen, Hoang
  • Österholm, Pär
  • Örebro University School of Business

Entstanden

  • 2022

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