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Identification-robust moment-based tests for Markov-switching in autoregressive models

Jean-Marie Dufour and Richard Luger

Cahiers de recherche from Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques

Abstract: This paper develops tests of the null hypothesis of linearity in the context of autoregressive models with Markov-switching means and variances. These tests are robust to the identification failures that plague conventional likelihood-based inference methods. The approach exploits the moments of normal mixtures implied by the regime-switching process and uses Monte Carlo test techniques to deal with the presence of an autoregressive component in the model specification. The proposed tests have very respectable power in comparison to the optimal tests for Markov-switching parameters of Carrasco et al. (2014) and they are also quite attractive owing to their computational simplicity. The new tests are illustrated with an empirical application to an autoregressive model of U.S. output growth.

Keywords: Mixture distributions; Markov chains; Regime switching; Parametric bootstrap; Monte Carlo tests; Exact inference. (search for similar items in EconPapers)
JEL-codes: C12 C15 C22 C52 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets and nep-ore
Date: 2017
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Related works:
Journal Article: Identification-robust moment-based tests for Markov switching in autoregressive models (2017) Downloads
Working Paper: Identification-robust moment-based tests for Markov-switching in autoregressive models (2016) Downloads
Working Paper: Identification-Robust Moment-Based Tests for Markov-Switching in Autoregressive Models (2016) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:lvl:crrecr:1701

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