A consistent test for conditional symmetry in time series models
A consistent test for conditional symmetry in time series models
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DOI：
10.1016/s0304-4076(01)00044-6
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发表时间：
2001-07
影响因子：
6.3
通讯作者：
Jushan Bai;Serena Ng
中科院分区：
文献类型：
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作者：
Jushan Bai;Serena Ng
拟极大似然法在验证ARCH/GARCH模型的自适应估计和一致性估计时，经常用到条件对称的假设。如果对称假设成立，施加条件对称可以提高自举的效率。本文提出了一种检验条件对称性的方法。所提出的检验不要求数据是平稳的或id的，并且条件变量的维度可以是无限的。所提出的检验是一致的，并且是渐近分布自由的。此外，该测试对根t局部替代方案具有非平凡的能力。即使对于小样本，测试的尺寸和功率也令人满意。将检验应用于各种时间序列，我们拒绝通货膨胀、汇率和股票收益的条件对称性。在考虑的非金融时间序列中，我们发现投资、耐用品消费和制造业就业也拒绝条件对称。
The assumption of conditional symmetry is often invoked to validate adaptive estimation and consistent estimation of ARCH/GARCH models by quasi-maximum likelihood. Imposing conditional symmetry can increase the efficiency of bootstraps if the symmetry assumption is valid. This paper proposes a procedure for testing conditional symmetry. The proposed test does not require the data to be stationary or i.i.d., and the dimension of the conditional variables can be infinite. The proposed test is consistent and is asymptotically distribution free. In addition, the test is shown to have nontrivial power against root-T local alternatives. The size and power of the test are satisfactory even for small samples. Applying the test to various time series, we reject conditional symmetry in inflation, exchange rate and stock returns. Among the nonfinancial time series considered, we find that investment, the consumption of durables, and manufacturing employment also reject conditional symmetry.