Hidden Markov models: Estimation theory and economic applications

In this thesis, maximum likelihood estimation of hidden Markov models in several settings is investigated. Nonparametric estimation of state-dependent general mixtures and log-concave densities is discussed theoretically and algorithmically. Penalized estimation for parametric hidden Markov models c...

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Bibliographic Details
Main Author: Leister, Anna Maria
Contributors: Holzmann, Hajo (Prof. Dr.) (Thesis advisor)
Format: Dissertation
Language:English
Published: Philipps-Universität Marburg 2016
Reine und Angewandte Mathematik
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Summary:In this thesis, maximum likelihood estimation of hidden Markov models in several settings is investigated. Nonparametric estimation of state-dependent general mixtures and log-concave densities is discussed theoretically and algorithmically. Penalized estimation for parametric hidden Markov models comparing several penalty functions is studied. In addition, various models based on mixture models and hidden Markov models differing in dependency structure and the inclusion of covariables are applied to a set of panel data containing the GDP of several countries.
Physical Description:126 Pages
DOI:https://doi.org/10.17192/z2016.0120