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
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Englisch
- Erschienen in
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Series: Working Paper ; No. 1/2022
- Klassifikation
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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
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Bayesian VAR
Heavy tails
GDP growth
Unemployment
- Ereignis
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Geistige Schöpfung
- (wer)
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Kiss, Tamás
Nguyen, Hoang
Österholm, Pär
- Ereignis
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Veröffentlichung
- (wer)
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Örebro University School of Business
- (wo)
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Örebro
- (wann)
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2022
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:43 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Kiss, Tamás
- Nguyen, Hoang
- Österholm, Pär
- Örebro University School of Business
Entstanden
- 2022