.png){kind=icon}
返回
Nonlinear factor models for network and panel data
.png){kind=icon}
OA
AI
DOI：10.1016/j.jeconom.2020.04.004.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Factor structures or interactive effects are convenient devices to incorporate latent variables in panel data models. We consider fixed effect estimation of nonlinear panel single-index models with factor structures in the unobservables, which include logit, probit, ordered probit and Poisson specifications. We establish that fixed effect estimators of model parameters and average partial effects have normal distributions when the two dimensions of the panel grow large, but might suffer from incidental parameter bias. We also show how models with factor structures can be applied to capture important features of network data such as reciprocity, degree heterogeneity, homophily in latent variables, and clustering. We illustrate this applicability with an empirical example to the estimation of a gravity equation of international trade between countries using a Poisson model with multiple factors. (c) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keyword:
Panel data
Network data
Interactive fixed effects
Factor models
Bias correction
Incidental parameter problem
Gravity equation
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:
5.2K
被引数:
3.0W
机构
引用论文
Jackknife and analytical bias reduction for nonlinear panel models非线性面板模型的折刀和分析偏差减少.png)
ECONOMETRICA
ECONOMETRICAIF7.1
PREAI
分享收藏
查看更多内容