Testing Constraints and Misspecification in VAR-ARCH Models

  • Wolfgang Polasek
  • Shuangzhe Liu
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Vector autoregressive models with conditional heteroskedastic errors (abbreviated as VAR-ARCH models) have become increasingly important for applications in financial econometrics. In this paper, we propose likelihood ratio and Wald tests for constraints and the White (1982) misspecification test for VAR-ARCH models which are estimated by the maximum likelihood (ML) method. The tests are discussed for a general class of multivariate conditional heteroskedastic time series models including the VAR-ARCH models. We derive the exact analytic expression for the gradient vector and the conditional information matrix from the log-likelihood function under the normality assumption.


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Copyright information

© Springer-Verlag Berlin · Heidelberg 2000

Authors and Affiliations

  • Wolfgang Polasek
    • 1
  • Shuangzhe Liu
    • 1
  1. 1.Institute of Statistics and EconometricsUniversity of BaselBaselSwitzerland

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