Heteroskedasticity, autocorrelation, and spatial correlation robust inference in linear panel models with fixed-effects
固定效应线性面板中对异方差、自相关与空间相关稳健的推断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：摘要直接研究个体与时间固定效应线性面板中的标准误和检验。
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
固定效应线性面板中对异方差、自相关与空间相关稳健的推断
- 研究问题
- 固定效应线性面板中，两类 HAC 标准误分别能处理哪些相关结构，固定带宽比例渐近近似能否改善检验的有限样本表现？
- 方法与思路
- 作者分析个体 HAC 的平均与截面平均的 HAC，针对个体及可能的时间固定效应，在大 T、固定或大截面下建立 fixed-b 理论。
- 材料陈述的贡献
- 摘要称，fixed-b 近似通常优于传统正态或卡方近似，尤以 Driscoll–Kraay 标准误明显，并有助于改善联合假设检验。
- 阅读关注点
- 应核对两类标准误的依赖条件：个体 HAC 平均不容许空间相关，截面平均的 HAC 要求时间弱依赖；另查带宽与临界值。
- 阅读边界
- 仅据所给出版社摘要，无法判断具体带宽、临界值或改善幅度；两类标准误的稳健范围不同，不能将其视为可无条件互换。
原文摘要
This paper develops an asymptotic theory for test statistics in linear panel models that are robust to heteroskedasticity, autocorrelation and/or spatial correlation. Two classes of standard errors are analyzed. Both are based on nonparametric heteroskedasticity autocorrelation (HAC) covariance matrix estimators. The first class is based on averages of HAC estimators across individuals in the cross-section, i.e. “averages of HACs”. This class includes the well known cluster standard errors analyzed by Arellano (1987) as a special case. The second class is based on the HAC of cross-section averages and was proposed by Driscoll and Kraay (1998). The ”HAC of averages” standard errors are robust to heteroskedasticity, serial correlation and spatial correlation but weak dependence in the time dimension is required. The “averages of HACs” standard errors are robust to heteroskedasticity and serial correlation including the nonstationary case but they are not valid in the presence of spatial correlation. The main contribution of the paper is to develop a fixed-b asymptotic theory for statistics based on both classes of standard errors in models with individual and possibly time fixed-effects dummy variables. The asymptotics is carried out for large time sample sizes for both fixed and large cross-section sample sizes. Extensive simulations show that the fixed-b approximation is usually much better than the traditional normal or chi-square approximation especially for the Driscoll–Kraay standard errors. The use of fixed-b critical values will lead to more reliable inference in practice especially for tests of joint hypotheses.