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On a mixture vector autoregressive model
Author(s) -
Fong P. W.,
Li W. K.,
Yau C. W.,
Wong C. S.
Publication year - 2007
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.5550350112
Subject(s) - univariate , autoregressive model , star model , multivariate statistics , context (archaeology) , nonlinear autoregressive exogenous model , setar , series (stratigraphy) , autoregressive integrated moving average , mathematics , econometrics , statistics , time series , computer science , paleontology , biology
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.