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On mixture double autoregressive time series models
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Li, Guodong, Zhu, Qianqian, Liu, Zhao and Li, Wai Keung (2017) On mixture double autoregressive time series models. Journal of Business & Economic Statistics, 35 (2). pp. 306-317. doi:10.1080/07350015.2015.1102735 ISSN 0735-0015.
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Official URL: http://dx.doi.org/10.1080/07350015.2015.1102735
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.
Item Type: | Journal Article | ||||||
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Divisions: | Faculty of Social Sciences > Warwick Business School > Finance Group Faculty of Social Sciences > Warwick Business School |
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Journal or Publication Title: | Journal of Business & Economic Statistics | ||||||
Publisher: | Routledge | ||||||
ISSN: | 0735-0015 | ||||||
Official Date: | 2017 | ||||||
Dates: |
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Volume: | 35 | ||||||
Number: | 2 | ||||||
Page Range: | pp. 306-317 | ||||||
DOI: | 10.1080/07350015.2015.1102735 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access |
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