Mixtures of autoregressive-autoregressive conditionally heteroscedastic models: semi-parametric approach

We propose data generating structures which can be represented as a mixture of autoregressive-autoregressive conditionally heteroscedastic models. The switching between the states is governed by a hidden Markov chain. We investigate semi-parametric estimators for estimating the functions based on th...

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Published in:Journal of applied statistics 2014-02, Vol.41 (2), p.275-293
Main Authors: Nademi, Arash, Farnoosh, Rahman
Format: Article
Language:English
Subjects:
EM algorithm
Estimating techniques
geometric ergodicity
hidden variables
Markov analysis
Mathematical models
Maximum likelihood method
mixture models
Parameter estimation
Regression analysis
semi-parametric autoregression
Studies
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description We propose data generating structures which can be represented as a mixture of autoregressive-autoregressive conditionally heteroscedastic models. The switching between the states is governed by a hidden Markov chain. We investigate semi-parametric estimators for estimating the functions based on the quasi-maximum likelihood approach and provide sufficient conditions for geometric ergodicity of the process. We also present an expectation-maximization algorithm for calculating the estimates numerically.
doi_str_mv 10.1080/02664763.2013.839129
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subjects EM algorithm
Estimating techniques
geometric ergodicity
hidden variables
Markov analysis
Mathematical models
Maximum likelihood method
mixture models
Parameter estimation
Regression analysis
semi-parametric autoregression
Studies
title Mixtures of autoregressive-autoregressive conditionally heteroscedastic models: semi-parametric approach
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