.png){kind=icon}
返回
Dynamic treatment effects
.png){kind=icon}
OA
AI
DOI：10.1016/j.jeconom.2015.12.001.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper develops robust models for estimating and interpreting treatment effects arising from both ordered and unordered multi-stage decision problems. Identification is secured through instrumental variables and/or conditional independence (matching) assumptions. We decompose treatment effects into direct effects and continuation values associated with moving to the next stage of a decision problem. Using our framework, we decompose the IV estimator, showing that IV generally does not estimate economically interpretable or policy-relevant parameters in prototypical dynamic discrete choice models, unless policy variables are instruments. Continuation values are an empirically important component of estimated total treatment effects of education. We use our analysis to estimate the components of what LATE estimates in a dynamic discrete choice model. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Choice theory
Dynamic treatment effects
Factor models
Marginal treatment effects
Regret
Conditional independence
Matching on mismeasured variables
Instrumental variables
Ordered choice models
Unordered choice models
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
Instrumental variable treatment of nonclassical measurement error models非经典测量误差模型的工具变量处理.png)
ECONOMETRICA
ECONOMETRICAIF7.1
PREAI
分享收藏
Understanding the Mechanisms Through Which an Influential Early Childhood Program Boosted Adult OutcomesAMERICAN ECONOMIC REVIEWOAAI
AMERICAN ECONOMIC REVIEWIF11.6
OAAI分享收藏分享收藏分享收藏分享收藏
查看更多内容