.png){kind=icon}
返回
Consistent covariance matrix estimation with spatially dependent panel data
.png){kind=icon}
PRE
AI
DOI：10.1162/003465398557825.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Many panel data sets encountered in macroeconomics, international economics, regional science, and finance are characterized by cross-sectional or spatial'' dependence. Standard techniques that fail to account for this dependence will result in inconsistently estimated standard errors. In this paper we present conditions under which a simple extension of common nonparametric covariance matrix estimation techniques yields standard error estimates that are robust to very general forms of spatial and temporal dependence as the time dimension becomes large. We illustrate the relevance of this approach using Monte Carlo simulations and a number of empirical examples.
Keyword:
PROCYCLICAL PRODUCTIVITY
HETEROSKEDASTICITY
RETURNS
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.8
论文数:
3.6K
被引数:
2.1W
机构
暂无机构信息
引用论文
分享收藏分享收藏分享收藏分享收藏AN IMPROVED HETEROSKEDASTICITY AND AUTOCORRELATION CONSISTENT COVARIANCE-MATRIX ESTIMATOR.png)
ECONOMETRICA
ECONOMETRICAIF7.1
PREAI
分享收藏
查看更多内容