EMPIRICAL LIKELIHOOD FOR AUTOREGRESSIVE MODELS, WITH APPLICATIONS TO UNSTABLE TIME SERIES
EMPIRICAL LIKELIHOOD FOR AUTOREGRESSIVE MODELS, WITH APPLICATIONS TO UNSTABLE TIME SERIES
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发表时间：
2002
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通讯作者：
Chin-Shan Chuang;N. Chan
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作者：
Chin-Shan Chuang;N. Chan
给出了一类自回归模型的经验似然估计，该模型的自回归模型的无因次构成了一个鞅差序列。建立了稳定和不稳定情况下对数经验似然比统计量的极限分布。对数经验似然比统计量的行为被考虑在几乎非平稳的模型中，以评估单位根检验的局部能力并构造可信区间。为了改善经验似然统计量的有限样本性能，提出了重采样方法。本文表明，经验似然方法与现有方法相比具有更好的优势，并展示了其对具有更一般新息结构的时间序列的潜力。
Empirical likelihood is developed for autoregressive models with inno- vations that form a martingale difference sequence. Limiting distributions of the log empirical likelihood ratio statistic for both the stable and unstable cases are established. Behavior of the log empirical likelihood ratio statistic is considered in nearly nonstationary models to assess the local power of unit root tests and to construct confidence intervals. Resampling methods are proposed to improve the finite-sample performance of empirical likelihood statistics. This paper shows that empirical likelihood methodology compares favorably with existing methods and demonstrates its potential for time series with more general innovation structures.