Comparing smooth transition and Markov switching autoregressive models of US unemployment
- Deschamps, Philippe J. Séminaire d'Économétrie, Université de Fribourg, Switzerland
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2007
37
Logistic smooth transition autoregressions
Hidden Markov models
Density forecasts
Markov chain Monte Carlo
Bridge sampling
Unemployment rate
English
Logistic smooth transition and Markov switching autoregressive models of a logistic transform of the monthly US unemployment rate are estimated by Markov chain Monte Carlo methods. The Markov switching model is identified by constraining the first autoregression coefficient to differ across regimes. The transition variable in the LSTAR model is the lagged seasonal difference of the unemployment rate. Out of sample forecasts are obtained from Bayesian predictive densities. Although both models provide very similar descriptions, Bayes factors and predictive efficiency tests (both Bayesian and classical) favor the smooth transition model.
- Faculty
- Faculté des sciences économiques et sociales et du management
- Department
- Département d'économie quantitative
- Language
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- English
- Classification
- Economics
- License
- License undefined
- Identifiers
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- RERO DOC 30797
- DOI 10.1002/jae.1014
- Persistent URL
- https://folia.unifr.ch/unifr/documents/302743
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