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Comparing smooth transition and Markov switching autoregressive models of US unemployment
Author(s) -
Deschamps Philippe J.
Publication year - 2008
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.1014
Subject(s) - autoregressive model , econometrics , markov chain monte carlo , markov chain , bayesian probability , unemployment , setar , economics , statistics , bayesian vector autoregression , unemployment rate , mathematics , star model , time series , autoregressive integrated moving average , economic growth
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. Copyright © 2008 John Wiley & Sons, Ltd.