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Comparing smooth transition and Markov switching autoregressive models of US Unemployment

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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.

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Bibliographic Info

Paper provided by Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland in its series DQE Working Papers with number 7.

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Length: 37 pages
Date of creation: 24 May 2007
Date of revision: 04 Jun 2008
Publication status: Published in the Journal of Applied Econometrics, 2008, vol. 23, no. 4, pp. 435-462.
Handle: RePEc:fri:dqewps:wp0007

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Keywords: Logistic smooth transition autoregressions; Hidden Markov models; Density forecasts; Markov chain Monte Carlo; Bridge sampling; Unemployment rate;

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