THE MOVING BLOCKS BOOTSTRAP FOR PANEL LINEAR REGRESSION MODELS WITH INDIVIDUAL FIXED EFFECTS
个体固定效应面板线性回归的移动区块自助法AI 解读 ↓
← 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：摘要直接研究个体固定效应面板回归及序列、横截面依赖下的自助推断。
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
个体固定效应面板线性回归的移动区块自助法
- 研究问题
- 当固定效应面板同时存在未知形式的序列与横截面依赖时，如何实施自助推断，并适应由横截面依赖强度决定的收敛速率？
- 方法与思路
- 作者将每个时点的所有个体观测组成向量，再沿时间实施移动区块自助法；允许弱混合时间依赖及强或弱横截面依赖。
- 材料陈述的贡献
- 摘要称，该方法允许横截面维度随时间维度非递减增长，学生化统计量无需预知横截面依赖强度；适当区块长度下模拟覆盖率良好。
- 阅读关注点
- 应核对联合渐近条件、时间混合与矩条件、学生化方差估计，以及区块长度选择如何影响不同依赖强度下的置信区间覆盖率。
- 阅读边界
- 仅据所给 Crossref 摘要，无法判断具体依赖限制和区块选择规则；良好覆盖率依赖适当区块长度，未给数值，刊本变化未说明。
原文摘要
In this paper we propose a bootstrap method for panel data linear regression models with individual fixed effects. The method consists of applying the standard moving blocks bootstrap of Künsch (1989, Annals of Statistics 17, 1217–1241) and Liu and Singh (1992, in R. LePage & L. Billiard (eds.), Exploring the Limits of the Bootstrap ) to the vector containing all the individual observations at each point in time. We show that this bootstrap is robust to serial and cross-sectional dependence of unknown form under the assumption that n (the cross-sectional dimension) is an arbitrary nondecreasing function of T (the time series dimension), where T → ∞, thus allowing for the possibility that both n and T diverge to infinity. The time series dependence is assumed to be weak (of the mixing type), but we allow the cross-sectional dependence to be either strong or weak (including the case where it is absent). Under appropriate conditions, we show that the fixed effects estimator (and also its bootstrap analogue) has a convergence rate that depends on the degree of cross-section dependence in the panel. Despite this, the same studentized test statistics can be computed without reference to the degree of cross-section dependence. Our simulation results show that the moving blocks bootstrap percentile- t intervals have very good coverage properties even when the degree of serial and cross-sectional correlation is large, provided the block size is appropriately chosen.