On a mixture vector autoregressive model

Canadian Journal of Statistics

Volumn 35, Issue 1, 2007, Pages 135-150

  Fong, P W ^a   Li, W K ^b   Yau, C W ^c   Wong, C S ^d  

^a HONG KONG MONETARY AUTHORITY   (Hong Kong)

^b UNIVERSITY OF HONG KONG   (Hong Kong)

^c Insurance Australia Group (IAG)   (Australia)

^d CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

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

Indexed keywords

EID: 34249287310     PISSN: 03195724     EISSN: None     Source Type: Journal    
DOI: 10.1002/cjs.5550350112     Document Type: Article

References (22)

1. V. E. Beneš (1967). Existence of finite invariant measures for Markov processes. Proceedings of the American Mathematical Society, 18, 1058-1061.
2. A. Berchtold (2003). Mixture transition distribution (MTD) modeling of heteroscedastic time series. Computational Statistics & Data Analysis, 41, 399-411.
3. A. Berchtold (2004). Optimisation of mixture models: comparison of different strategies. Computational Statistics, 19, 385-406.
4. C. Biernacki, G. Celeux & G. Govaert (2003). Choosing starting value for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models. Computational Statistics & Data Analysis, 41, 561-575.
5. G. E. P. Box, G. M. Jenkins & G. C. Reinsel (1994). Time Series Analysis: Forecasting and Control, 3rd Edition, Prentice-Hall, Englewood Cliffs, NJ.
6. X. Carvalho & M. A. Tanner (2005). Modeling nonlinear time series with local mixtures of generalized linear models. The Canadian Journal of Statistics, 33, 97-113.
7. P. W. Fong, W. K. Li, C. W. Yau & C. S. Wong (2004). On a Mixture Vector Autoregressive Model. Research Report no 369, Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong, China.
8. S. Goldberg (1958). Difference Equations. Wiley, New York.
9. J. C. Hull (2002). Fundamentals of Futures and Options Markets. Prentice Hall, Englewood Cliffs, NJ.
10. M. Lanne (2006). Nonlinear dynamics of interest rate and inflation. Journal of Applied Econometrics, 21, 1157-1168.
11. M. Lanne & P. Saikkonen (2003). Modeling the U.S. short-term interest rate by mixture autoregressive processes. Journal of Financial Econometrics, 1, 96-125.
12. N. D. Le, R. D. Martin & A. E. Raftery (1996). Modeling flat stretches, bursts, and outliers in time series using mixture transition distribution models. Journal of the American Statistical Association, 91, 1504-1514.
13. W. K. Li & T. K. Mak (1994). On the squared residual autocorrelations in non-linear time series with conditional heteroscedasticity. Journal of Time Series Analysis, 15, 627-636.
14. G. J. McLachlan & K. E. Basford (1988). Mixture Models: Inference and Application to Clustering. Marcel Dekker, New York.
15. V. L. Martin (1992). Threshold time series models as multimodal distribution jump processes. Journal of Time Series Analysis, 13, 79-94.
16. P. Saikkonen (2007). Stability of mixtures of vector autoregressions with autoregressive conditional heteroskedasticity. Statistica Sinica, 17, 221-239.
17. D. M. Titterington, A. F. M. Smith & U. E. Makov (1985). Statistical Analysis of Finite Mixture Distributions. Wiley, New York.
18. R. S. Tsay (2002), Analysis of Financial Time Series. Wiley, New York.
19. C. S. Wong & W. K. Li (2000). On a mixture autoregressive model. Journal of the Royal Statistical Society Series B, 62, 95-115.
20. C. S. Wong & W. K. Li (2001a). On a mixture autoregressive conditional heteroscedastic model. Journal of the American Statistical Association, 96, 982-995.
21. C. S. Wong & W. K. Li (2001b). A mixture time series model for the Heng Sang Index. Hong Kong Statistical Society Bulletin, 23, 6-9.
22. C. S. Wong & W. K. Li (2001c). On a logistic mixture autoregressive model. Biometrika, 88, 833-846