On mixture double autoregressive time series models

Guodong LI, Qianqian ZHU, Zhao LIU, Wai Keung LI

Research output: Contribution to journalArticles

5 Citations (Scopus)

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. Copyright © 2017 American Statistical Association.
Original languageEnglish
Pages (from-to)306-317
JournalJournal of Business and Economic Statistics
Volume35
Issue number2
Early online dateMar 2017
DOIs
Publication statusPublished - 2017

Citation

Li, G., Zhu, Q., Liu, Z., & Li, W. K. (2017). On mixture double autoregressive time series models. Journal of Business & Economic Statistics, 35(2), 306-317. doi: 10.1080/07350015.2015.1102735

Keywords

  • Double autoregressive model
  • EM algorithm
  • Mixture model
  • Stationarity

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