Forecasting with non-homogeneous hidden Markov models

Authors:
Loukia Meligkotsidou;Petros Dellaportas
Affiliations:
Department of Mathematics, University of Athens, Athens, Greece;Department of Statistics, Athens University of Economics and Business, Athens, Greece
Venue:
Statistics and Computing
Year:
2011

Citing 1
Cited 1

Inference in Hidden Markov Models (Springer Series in Statistics)

Inference in Hidden Markov Models (Springer Series in Statistics)

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Computational Statistics & Data Analysis

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Abstract

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.