Preprint

A flexible prior distribution for Markov switching autoregressions with Student-t errors

    2005

44

English This paper proposes an empirical Bayes approach for Markov switching autoregressions that can constrain some of the state-dependent parameters (regression coefficients and error variances) to be approximately equal across regimes. By flexibly reducing the dimension of the parameter space, this can help to ensure regime separation and to detect the Markov switching nature of the data. The permutation sampler with a hierarchical prior is used for choosing the prior moments, the identification constraint, and the parameters governing prior state dependence. The empirical relevance of the methodology is illustrated with an application to quarterly and monthly real interest rate data.
Faculty
Faculté des sciences économiques et sociales
Department
Département d'économie quantitative
Language
  • English
Classification
Economics
License
License undefined
Identifiers
  • RERO DOC 30793
Persistent URL
https://folia.unifr.ch/unifr/documents/302723
Statistics

Document views: 7 File downloads:
  • WP_DQE_02.pdf: 1