.png){kind=icon}
返回
Spatial dynamic panel data models with correlated random effects
.png){kind=icon}
OA
AI
DOI：10.1016/j.jeconom.2020.05.016.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, M-estimation and inference methods are developed for spatial dynamic panel data models with correlated random effects, based on short panels. The unobserved individual-specific effects are assumed to be correlated with the observed time-varying regressors linearly or in a linearizable way, giving the so-called correlated random effects model, which allows the estimation of effects of time-invariant regressors. The unbiased estimating functions are obtained by adjusting the conditional quasi-scores given the initial observations, leading to M-estimators that are consistent, asymptotically normal, and free from the initial conditions except the process starting time. By decomposing the estimating functions into sums of terms uncorrelated given idiosyncratic errors, a hybrid method is developed for consistently estimating the variance-covariance matrix of the M-estimators, which again depends only on the process starting time. Monte Carlo results demonstrate that the proposed methods perform well in finite sample. An empirical application on the political competition in China is presented. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Adjusted quasi score
Dynamic panels
Correlated random effects
Initial-conditions
Martingale difference
Spatial effects
Short panels
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:
4
论文数:
5.3K
被引数:
3.0W
机构
引用论文
Ground-Dwelling Arthropod Community Responses to Recent and Repeated Wildfires in Conifer Forests of Northern New Mexico, USA.png)
ForestsOAAI
ForestsIF0
OAAI分享收藏
查看更多内容