Markov switching model of nonlinear autoregressive with skew-symmetric innovations

ABSTRACT

We consider data generating structures which can be represented as a Markov switching of nonlinear autoregressive model with considering skew-symmetric innovations such that switching between the states is controlled by a hidden Markov chain. We propose semi-parametric estimators for the nonlinear functions of the proposed model based on a maximum likelihood (ML) approach and study sufficient conditions for geometric ergodicity of the process. Also, an Expectation-Maximization type optimization for obtaining the ML estimators are presented. A simulation study and a real world application are also performed to illustrate and evaluate the proposed methodology.

KEYWORDS:

• Markov switching models
• semi-parametric autoregression
• skew symmetric innovations
• EM algorithm
• geometric ergodicity

2010 MATHEMATICS SUBJECT CLASSIFICATIONS:

• 37M10
• 62F10
• 62-07
• 62G05

Acknowledgements

The author is grateful to the editor and anonymous referees for helpful comments and suggestions on this article.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

The authors advise no direct funding is associated with the research reported on this article.