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On a constrained mixture vector autoregressive model

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  • Wong, C.S.

Abstract

A mixture vector autoregressive model has recently been introduced to the literature. Although this model is a promising candidate for nonlinear multiple time series modeling, high dimensionality of the parameters and lack of method for computing the standard errors of estimates limit its application to real data. The contribution of this paper is threefold. First, a form of parameter constraints is introduced with an efficient EM algorithm for estimation. Second, an accurate method for computing standard errors is presented for the model with and without parameter constraints. Lastly, a hypothesis-testing approach based on likelihood ratio tests is proposed, which aids in the selection of unnecessary parameters and leads to the greater efficiency at the estimation. A case study employing U.S. Treasury constant maturity rates illustrates the applicability of the mixture vector autoregressive model with parameter constraints, and the importance of using a reliable method to compute standard errors.

Suggested Citation

  • Wong, C.S., 2013. "On a constrained mixture vector autoregressive model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 93(C), pages 19-28.
    • Handle: RePEc:eee:matcom:v:93:y:2013:i:c:p:19-28
    DOI: 10.1016/j.matcom.2013.05.001
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    References listed on IDEAS

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    1. Wong, C.S., 2011. "Modeling Hong Kong’s stock index with the Student t-mixture autoregressive model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1334-1343.
    2. C. S. Wong & W. K. Li, 2000. "On a mixture autoregressive model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 95-115.
    3. C. S. Wong & W. S. Chan & P. L. Kam, 2009. "A Student t-mixture autoregressive model with applications to heavy-tailed financial data," Biometrika, Biometrika Trust, vol. 96(3), pages 751-760.
    4. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Michael McAleer & Felix Chan & Les Oxley, 2013. "Modeling and Simulation: An Overview," Working Papers in Economics 13/18, University of Canterbury, Department of Economics and Finance.

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