TOWARD A UNIFIED INTERVAL ESTIMATION OF AUTOREGRESSIONS
提出一种基于经验似然的置信区间方法，用于一阶自回归模型的系数区间估计，无论过程是平稳、单位根、近整合还是爆炸性，该方法均适用，为AR(1)模型提供了统一的区间估计框架。
An empirical likelihood–based confidence interval is proposed for interval estimations of the autoregressive coefficient of a first-order autoregressive model via weighted score equations. Although the proposed weighted estimate is less efficient than the usual least squares estimate, its asymptotic limit is always normal without assuming stationarity of the process. Unlike the bootstrap method or the least squares procedure, the proposed empirical likelihood–based confidence interval is applicable regardless of whether the underlying autoregressive process is stationary, unit root, near-integrated, or even explosive, thereby providing a unified approach for interval estimation of an AR(1) model to encompass all situations. Finite-sample simulation studies confirm the effectiveness of the proposed method.