How much should we trust differences-in-differences estimates?
How much should we trust differences-in-differences estimates?
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DOI：
10.1162/003355304772839588
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发表时间：
2004-02-01
影响因子：
13.7
通讯作者：
Mullainathan, S
中科院分区：
文献类型：
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作者：
Bertrand, M;Duflo, E;Mullainathan, S
大多数采用差异性估计(DD)的论文使用多年的数据，并关注序列相关的结果，但忽略了由此产生的标准误差是不一致的。为了说明这个问题的严重性，我们在当前人口调查中关于女性工资的州级数据中随机生成了安慰剂法律。对于每个定律，我们使用OLS来计算其“效应”的DD估计以及该估计的标准误差。这些传统的DD标准误差严重低估了估计者的标准差：我们发现，对于高达45%的安慰剂干预，在5%的水平上有显著的“效果”。我们使用蒙特卡罗模拟来调查现有方法如何帮助解决这个问题。在时间序列过程中设定特定参数形式的计量经济学修正表现不佳。当状态数足够大时，Bootstrap(考虑数据的自相关)工作得很好。基于方差-协方差矩阵的渐近近似的两个校正对于中等数量的状态很好地工作，而一个校正将时间序列信息折叠成“前”和“后”周期，并且明确地考虑到有效样本大小即使对于少量的状态也很好地工作。
Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data on female wages from the Current Population Survey. For each law, we use OLS to compute the DD estimate of its "effect" as well as the standard error of this estimate. These conventional DD standard errors severely understate the standard deviation of the estimators: we find an "effect" significant at the 5 percent level for up to 45 percent of the placebo interventions. We use Monte Carlo simulations to investigate how well existing methods help solve this problem. Econometric corrections that place a specific parametric form on the time-series process do not perform well. Bootstrap (taking into account the autocorrelation of the data) works well when the number of states is large enough. Two corrections based on asymptotic approximation of the variance-covariance matrix work well for moderate numbers of states and one correction that collapses the time series information into a "pre"-and "post"-period and explicitly takes into account the effective sample size works well even for small numbers of states.