.png){kind=icon}
返回
Consistent model specification tests based on k-nearest-neighbor estimation method
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jeconom.2016.03.004.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We propose a simple consistent test for a, parametric regression functional form based on k-nearest-neighbor (k-nn) method. We derive the null distribution of the test statistic and show that the test achieves the minimax rate optimality against smooth alternatives. A wild bootstrap method is used to better approximate the null distribution of the test statistic. We also propose a k-nn statistic which tests for omitted variables nonparametrically. Simulations and an empirical application using US economics new Ph.D. job market matching data show that the k-nn method is more appropriate than the kernel method to analyze unevenly distributed data. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
k-nearest-neighbor method
Consistent test
Bootstrap
Empirical application
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
.png){kind=icon}
分享收藏分享收藏
查看更多内容