Statistics and Computing

, Volume 21, Issue 3, pp 439–449 | Cite as

Forecasting with non-homogeneous hidden Markov models

  • Loukia Meligkotsidou
  • Petros Dellaportas


We present a Bayesian forecasting methodology of discrete-time finite state-space hidden Markov models with non-constant transition matrix that depends on a set of exogenous covariates. We describe an MCMC reversible jump algorithm for predictive inference, allowing for model uncertainty regarding the set of covariates that affect the transition matrix. We apply our models to interest rates and we show that our general model formulation improves the predictive ability of standard homogeneous hidden Markov models.


Exogenous variables Markov chain Monte Carlo Multinomial regression Prediction Reversible jump 


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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of AthensAthensGreece
  2. 2.Department of StatisticsAthens University of Economics and BusinessAthensGreece

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