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来源类型Working Paper
规范类型报告
DOI10.3386/w20673
来源IDWorking Paper 20673
Measuring the Sensitivity of Parameter Estimates to Estimation Moments
Isaiah Andrews; Matthew Gentzkow; Jesse M. Shapiro
发表日期2014-11-17
出版年2014
语种英语
摘要We propose a local measure of the relationship between parameter estimates and the moments of the data they depend on. Our measure can be computed at negligible cost even for complex structural models. We argue that reporting this measure can increase the transparency of structural estimates, making it easier for readers to predict the way violations of identifying assumptions would affect the results. When the key assumptions are orthogonality between error terms and excluded instruments, we show that our measure provides a natural extension of the omitted variables bias formula for nonlinear models. We illustrate with applications to published articles in several fields of economics.
主题Econometrics ; Estimation Methods
URLhttps://www.nber.org/papers/w20673
来源智库National Bureau of Economic Research (United States)
引用统计
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/578347
推荐引用方式
GB/T 7714
Isaiah Andrews,Matthew Gentzkow,Jesse M. Shapiro. Measuring the Sensitivity of Parameter Estimates to Estimation Moments. 2014.
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