Comparing smooth transition and Markov switching autoregressive models of US Unemployment

Philippe J. Deschamps

No 7, DQE Working Papers from Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland

Abstract: 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.

Keywords: Logistic smooth transition autoregressions; Hidden Markov models; Density forecasts; Markov chain Monte Carlo; Bridge sampling; Unemployment rate (search for similar items in EconPapers)
JEL-codes: C11 C22 C53 E24 E27 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-lab and nep-mac
Date: 2007-05-24, Revised 2008-06-04
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Published in the Journal of Applied Econometrics, 2008, vol. 23, no. 4, pp. 435-462.

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Journal Article: Comparing smooth transition and Markov switching autoregressive models of US unemployment (2008)
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