Author: Nademi Arash Farnoosh Rahman
Publisher: Taylor & Francis Ltd
ISSN: 1360-0532
Source: Journal of Applied Statistics, Vol.41, Iss.2, 2014-02, pp. : 275-293
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Abstract
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.