conditional heteroskedasticity;
model misspecification test;
nonlinear time series;
parameter constancy;
smooth transition GARCH;
D O I:
10.1016/S0304-4076(02)00096-9
中图分类号:
F [经济];
学科分类号:
02 ;
摘要:
In this paper, a unified framework for testing the adequacy of an estimated GARCH model is presented. Parametric Lagrange multiplier (LM) or LM type tests of no ARCH in standardized errors, linearity, and parameter constancy are proposed. The asymptotic null distributions of the tests are standard, which makes application easy. Versions of the tests that are robust against normormal errors are provided. The finite sample properties of the test statistics are investigated by simulation. The robust tests prove superior to the nonrobust ones when the errors are nonnormal. They also compare favourably in terms of power with misspecification tests previously proposed in the literature. (C) 2002 Elsevier Science B.V. All rights reserved.
机构:
Univ Western Ontario, Dept Stat & Actuarial Sci, London, ON N6A 5B7, CanadaUniv Western Ontario, Dept Stat & Actuarial Sci, London, ON N6A 5B7, Canada
Escobar-Anel, Marcos
Rastegari, Javad
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机构:
Univ Western Ontario, Dept Stat & Actuarial Sci, London, ON N6A 5B7, CanadaUniv Western Ontario, Dept Stat & Actuarial Sci, London, ON N6A 5B7, Canada
Rastegari, Javad
Stentoft, Lars
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机构:
Univ Western Ontario, Dept Stat & Actuarial Sci, London, ON N6A 5B7, Canada
Univ Western Ontario, Social Sci Ctr, Dept Econ, London, ON N6A 5C2, CanadaUniv Western Ontario, Dept Stat & Actuarial Sci, London, ON N6A 5B7, Canada
机构:
Department of Information Systems, Business Statistics and Operations Management, Hong Kong University of Science and Technology, Hong Kong, Hong KongDepartment of Information Systems, Business Statistics and Operations Management, Hong Kong University of Science and Technology, Hong Kong, Hong Kong
So, Mike K. P.
Yip, Iris W. H.
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h-index: 0
机构:
Department of Information Systems, Business Statistics and Operations Management, Hong Kong University of Science and Technology, Hong Kong, Hong KongDepartment of Information Systems, Business Statistics and Operations Management, Hong Kong University of Science and Technology, Hong Kong, Hong Kong
Yip, Iris W. H.
Journal of Forecasting,
2012,
31
(05):
: 443
-
468