On a mixture vector autoregressive model

P. W. FONG, Wai Keung LI, C. W. YAU, C. S. WONG

Research output: Contribution to journalArticles

24 Citations (Scopus)

Abstract

The authors show how to extend univariate mixture autoregressive models to a multivariate time series context. Similar to the univariate case, the multivariate model consists of a mixture of stationary or nonstationary autoregressive components. The authors give the first and second order stationarity conditions for a multivariate case up to order 2. They also derive the second order stationarity condition for the univariate mixture model up to arbitrary order. They describe an EM algorithm for estimation, as well as a diagnostic checking procedure. They study the performance of their method via simulations and include a real application. Copyright © 2007 Statistical Society of Canada.
Original languageEnglish
Pages (from-to)135-150
JournalCanadian Journal of Statistics
Volume35
Issue number1
DOIs
Publication statusPublished - Mar 2007

Citation

Fong, P. W., Li, W. K., Yau, C. W., & Wong, C. S. (2007). On a mixture vector autoregressive model. Canadian Journal of Statistics, 35(1), 135-150. doi: 10.1002/cjs.5550350112

Keywords

  • Diagnostic checking
  • EM algorithm
  • Mixture vector autoregressive model
  • Multivariate time series
  • Stationarity

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