Residual-Based Tests for Normality in Autoregressions: Asymptotic Theory and Simulation Evidence
Residual-Based Tests for Normality in Autoregressions: Asymptotic Theory and Simulation Evidence
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DOI：
10.1080/07350015.2000.10524846
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发表时间：
2000-01
影响因子：
3
通讯作者：
L. Kilian;U. Demiroğlu
中科院分区：
文献类型：
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作者：
L. Kilian;U. Demiroğlu
向量自回归（VAR）模型中Jarque-Bera检验的渐近有效性的现有结果假设平稳性。然而，在应用工作中，研究人员经常与可能整合和协整合的过程。我们证明了向量误差校正（VEC）模型和无限制VAR模型的Jarque-Bera检验的渐近有效性可能整合或协整变量。我们还建议在平稳VAR模型和VEC模型中使用自举临界值。我们表明，自举版本的Jarque-Bera测试是相当准确的小样本比渐近测试，即使是根接近统一的过程。
Existing results for the asymptotic validity of the Jarque–Bera test in vector autoregressive (VAR) models assume stationarity. In applied work, however, researchers often work with possibly integrated and cointegrated process. We prove the asymptotic validity of the Jarque–Bera test for vector error-correction (VEC) models and for unrestricted VAR models with possibly integrated or cointegrated variables. We also propose the use of bootstrap critical values in stationary VAR models and in VEC models. We show that the bootstrap version of the Jarque–Bera test is considerably more accurate in small samples than the asymptotic test, even for processes with roots close to unity.