On a mixture vector autoregressive model |
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Authors: | P W Fong W K Li C W Yau C S Wong |
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Institution: | 1. Research Department, Hong Kong Monetary Authority Hong Kong, China;2. Department of Statistics and Actuarial Science The University of Hong Kong, Pokfulam Road, Hong Kong, China;3. Research and Development Department, Insurance Australia Group Level 28, 388 George Street, Sydney, NSW 2000 Australia;4. Department of Finance The Chinese University of Hong Kong, Shatin, Hong Kong, China |
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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. |
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Keywords: | Diagnostic checking EM algorithm mixture vector autoregressive model multivariate time series stationarity |
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