跳到正文
← 返回论文列表与筛选
阅读说明

本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。

引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。

关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。

分类依据与纠错

AI 根据所提供的论文材料归类:摘要直接研究受边界限制的时间序列单位根检验,并以美国利率数据示例,最直接对应时间序列方法方向。

反馈分类问题

正在核对关联版本…

代码、数据与补充材料

正在核对对应版本的资源…

关注这篇论文的作者
Giuseppe Cavaliere
Fang Xu

有界时间序列的单位根检验

研究问题
经济金融序列受构造或政策边界限制时,传统单位根检验可能过度拒绝原假设;如何构建能够控制尺寸的检验?
方法与思路
作者提出适用于有界序列的增广 Dickey–Fuller 型检验与修改后的 M 检验,并通过模拟获取相应临界值。
材料陈述的贡献
摘要称,新检验可渐近控制尺寸,模拟中表现良好且优于所比较的 Phillips–Perron 型检验,并以美国利率示例。
阅读关注点
应核对上下界及边界机制如何进入极限分布、模拟临界值所需参数,以及滞后选择和有限样本比较设置。
阅读边界
仅据所给出版社摘要,无法判断边界已知性、变化边界或政策调整的处理;比较优势限于摘要所述模拟范围。

原文摘要

Many key economic and financial series are bounded either by construction or through policy controls. Conventional unit root tests are potentially unreliable in the presence of bounds, since they tend to over-reject the null hypothesis of a unit root, even asymptotically. So far, very little work has been undertaken to develop unit root tests which can be applied to bounded time series. In this paper we address this gap in the literature by proposing unit root tests which are valid in the presence of bounds. We present new augmented Dickey–Fuller type tests as well as new versions of the modified ‘M’ tests developed by Ng and Perron [Ng, S., Perron, P., 2001. LAG length selection and the construction of unit root tests with good size and power. Econometrica 69, 1519–1554] and demonstrate how these tests, combined with a simulation-based method to retrieve the relevant critical values, make it possible to control size asymptotically. A Monte Carlo study suggests that the proposed tests perform well in finite samples. Moreover, the tests outperform the Phillips–Perron type tests originally proposed in Cavaliere [Cavaliere, G., 2005. Limited time series with a unit root. Econometric Theory 21, 907–945]. An illustrative application to U.S. interest rate data is provided.

笔记与分组
相关领域论文