GAUSSIAN INFERENCE IN AR(1) TIME SERIES WITH OR WITHOUT A UNIT ROOT
GAUSSIAN INFERENCE IN AR(1) TIME SERIES WITH OR WITHOUT A UNIT ROOT
复制标题
带或不带单位根的 AR(1) 时间序列中的高斯推理
DOI：
10.1017/s0266466608080262
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发表时间：
2006
影响因子：
0.8
通讯作者：
Chirok Han
中科院分区：
文献类型：
--
作者：
P. Phillips;Chirok Han
本文介绍了一种简单的基于一阶差分的AR（1）模型的估计和推断方法。估计几乎没有有限样本偏差，对初始条件不敏感，该方法具有不寻常的优势，即高斯中心极限理论适用，并且随着自回归系数以均匀的$\sqrt{n}$收敛速度通过统一，该方法是连续的。在此过程中，继菲利普斯和索洛（1992，Annals of Statistics，20，971-1001）之后，给出了线性过程样本协方差的一个有用的中心极限定理（CLT）。该方法还具有对动态面板的有用扩展。
This paper introduces a simple first-difference-based approach to estimation and inference for the AR(1) model. The estimates have virtually no finite-sample bias and are not sensitive to initial conditions, and the approach has the unusual advantage that a Gaussian central limit theory applies and is continuous as the autoregressive coefficient passes through unity with a uniform $\sqrt{n}$ rate of convergence. En route, a useful central limit theorem (CLT) for sample covariances of linear processes is given, following Phillips and Solo (1992, Annals of Statistics, 20, 971–1001). The approach also has useful extensions to dynamic panels.