.png){kind=icon}
返回
Prediction in an Unbalanced Nested Error Components Panel Data Model
.png){kind=icon}
PRE
AI
DOI：10.1002/for.2272.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper derives the best linear unbiased predictor for an unbalanced nested error components panel data model. This predictor is useful in many econometric applications that are usually based on unbalanced panel data and have a nested (hierarchical) structure. Examples include predicting student performance in a class in a school, or house prices in a neighborhood in a county or a state. Using Monte Carlo simulations, we show that this predictor is better in root mean square error performance than the usual fixed- or random-effects predictors ignoring the nested structure of the data. This is applied to forecasting the productivity of public capital in the private sector using nested panel data of 48 contiguous American states. Copyright (c) 2013 John Wiley & Sons, Ltd.
Keyword:
nested error components
unbalanced panels
forecasting
linear predictor
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:
2.7
论文数:
2.3K
被引数:
3.0K
机构
引用论文
.png){kind=icon}
查看更多内容