Bayesian inference and state number determination for hidden Markov models: an application to the information content of the yield curve about inflation

DOI10.1016/j.jeconom.2003.12.010zbMath1084.62021OpenAlexW1990134292MaRDI QIDQ1886287

Florian Pelgrin, Nicolas Chopin

Publication date: 18 November 2004

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jeconom.2003.12.010

zbMATH Keywords

Switching regression modelsHidden Markov modelsInformation content of the yield curveParticle filtersState number determination

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05)

Related Items

Methods for inference in large multiple-equation Markov-switching models ⋮ A Survey of Sequential Monte Carlo Methods for Economics and Finance ⋮ A decision-theoretic approach for segmental classification ⋮ Parallel sequential Monte Carlo samplers and estimation of the number of states in a hidden Markov model ⋮ Real time detection of structural breaks in GARCH models ⋮ Quantifying the uncertainty in change points ⋮ Inference and Model Choice for Sequentially Ordered Hidden Markov Models ⋮ Dynamic detection of change points in long time series ⋮ Temporal aggregation of Markov-switching financial return models ⋮ Discrete-time implementation of continuous-time filters with application to regime-switching dynamics estimation ⋮ Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach

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