On Mixture Double Autoregressive Time Series Models

Guodong Li, Qianqian Zhu, Zhao Liu and Wai Keung Li

Journal of Business & Economic Statistics, 2017, vol. 35, issue 2, 306-317

Abstract: This article proposes a mixture double autoregressive model by introducing the flexibility of mixture models to the double autoregressive model, a novel conditional heteroscedastic model recently proposed in the literature. To make it more flexible, the mixing proportions are further assumed to be time varying, and probabilistic properties including strict stationarity and higher order moments are derived. Inference tools including the maximum likelihood estimation, an expectation–maximization (EM) algorithm for searching the estimator and an information criterion for model selection are carefully studied for the logistic mixture double autoregressive model, which has two components and is encountered more frequently in practice. Monte Carlo experiments give further support to the new models, and the analysis of an empirical example is also reported.

Date: 2017
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:35:y:2017:i:2:p:306-317

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DOI: 10.1080/07350015.2015.1102735

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