22 Pages Posted: 1 Jan 2017
Date Written: December 30, 2016
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-Hu-Ploberger (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
JEL Classification: C12, C15, C22, C52
Suggested Citation: Suggested Citation
Dufour, Jean-Marie and Luger, Richard, Identification-Robust Moment-Based Tests for Markov-Switching in Autoregressive Models (December 30, 2016). Available at SSRN: https://ssrn.com/abstract=2891810