G2TT
来源类型Discussion paper
规范类型论文
来源IDDP17429
DP17429 Improved Causal Inference on Spatial Observations: A Smoothing Spline Approach
Morgan Kelly
发表日期2022-07-03
出版年2022
语种英语
摘要With geographical observations, nearby places often have very similar treatments, controls, and outcomes. In such cases, even with perfect identification, difference in differences and synthetic controls return imprecise coefficients, while regression discontinuities and instrumental variables are prone to severe bias and spurious significance. This paper shows how this may be remedied by adding a spatial smoothing spline to the regression, something easily implemented in practice. The spline allows spatial structure to be separated out as a nuisance variable while simultaneously improving the bias-variance trade-off for the parameters of interest. For simulations and real examples, including a spline causes a marked shrinkage of coefficients, while standard errors change little for most types of cross-section but fall for panels.
主题Economic History
URLhttps://cepr.org/publications/dp17429
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/546513
推荐引用方式
GB/T 7714
Morgan Kelly. DP17429 Improved Causal Inference on Spatial Observations: A Smoothing Spline Approach. 2022.
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