.png){kind=icon}
.png){kind=icon}
返回
Testing identifying assumptions in nonseparable panel data models
.png){kind=icon}
OA
AI
DOI：10.1016/j.jeconom.2016.11.005.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recent work on nonparametric identification of average partial effects (APEs) from panel data require restrictions on individual or time heterogeneity. Identifying assumptions under the generalized first-differencing category, such as time homogeneity (Chernozhukov et al., 2013), have testable equality restrictions on the distribution of the outcome variable. This paper proposes specification tests based on these restrictions. The bootstrap critical values for the resulting Kolmogorov-Smirnov and Cramer-von-Mises statistics are shown to be asymptotically valid and deliver good finite-sample properties in Monte Carlo simulations. An empirical application illustrates the merits of testing nonparametric identification from an empiricist's perspective. (C) Elsevier B.V. All rights reserved.
Keyword:
Panel data
Nonparametric identification
Specification testing
Discrete regressors
Bootstrap adjustment
Kolmogorov Smirnov statistic
Cramer-von-Mises statistic
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。
