Markov-Switching Models with State-Dependent Time-Varying Transition Probabilities

Zacharias Psaradakis and Martin Sola

No 1702, Birkbeck Working Papers in Economics and Finance from Birkbeck, Department of Economics, Mathematics & Statistics

Abstract: This paper proposes a model which allows for discrete stochastic breaks in the time-varying transition probabilities of Markov-switching models with autoregressive dynamics. An extensive simulation study is undertaken to examine the properties of the maximum-likelihood estimator and related statistics, and to investigate the implications of misspecification due to unaccounted changes in the parameters of the Markov transition mechanism. An empirical application that examines the relationship between Argentinian sovereign bond spreads and output growth is also discussed.

Keywords: Markov-switching models; Maximum likelihood; Monte Carlo experiments; Time-varying transition probabilities. (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
Date: 2017-03
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: Add references at CitEc
Citations:

Downloads: (external link)
https://eprints.bbk.ac.uk/id/eprint/19116 First version, 2017 (application/pdf)

Related works:
Journal Article: Markov-Switching Models with State-Dependent Time-Varying Transition Probabilities (2024)
Working Paper: Markov-Switching Models with State-Dependent Time-Varying Transition Probabilities (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bbk:bbkefp:1702

Ordering information: This working paper can be ordered from

Access Statistics for this paper

More papers in Birkbeck Working Papers in Economics and Finance from Birkbeck, Department of Economics, Mathematics & Statistics Malet Street, London WC1E 7HX, UK.
Bibliographic data for series maintained by ( this e-mail address is bad, please contact ).