Preprint

Comparing smooth transition and Markov switching autoregressive models of US unemployment

    2007

37

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
Department
Département d'économie quantitative
Language
  • English
Classification
Economics
License
License undefined
Identifiers
  • RERO DOC 30797
Persistent URL
https://folia.unifr.ch/unifr/documents/302743
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