Causal models for longitudinal and panel data: a survey

被引:1
|
作者
Arkhangelsky, Dmitry [1 ]
Imbens, Guido [2 ,3 ]
机构
[1] CEMFI, Casado Alsisal 5, Madrid 28014, Spain
[2] Stanford Univ, Grad Sch Business, 655 Knight Way, Stanford, CA 94305 USA
[3] Stanford Univ, Dept Econ, 655 Knight Way, Stanford, CA 94305 USA
来源
ECONOMETRICS JOURNAL | 2024年 / 27卷 / 03期
关键词
Causal effects; difference in differences; factor models; panel data; synthetic control methods; two-way fixed effects; DIFFERENCE-IN-DIFFERENCES; DYNAMIC DISCRETE-CHOICE; SYNTHETIC CONTROL METHOD; TIME-SERIES; PROPENSITY SCORE; CROSS-SECTION; TRAINING-PROGRAMS; REGRESSION-MODELS; LINEAR-REGRESSION; POLICY EVALUATION;
D O I
10.1093/ectj/utae014
中图分类号
F [经济];
学科分类号
02 ;
摘要
In this survey we discuss the recent causal panel data literature. This recent literature has focused on credibly estimating causal effects of binary interventions in settings with longitudinal data, emphasising practical advice for empirical researchers. It pays particular attention to heterogeneity in the causal effects, often in situations where few units are treated and with particular structures on the assignment pattern. The literature has extended earlier work on difference-in-differences or two-way fixed effect estimators. It has more generally incorporated factor models or interactive fixed effects. It has also developed novel methods using synthetic control approaches.
引用
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页码:C1 / C61
页数:61
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