是否有人使用Wald检验来检查平行趋势,作为差异中的差异的预检验阶段?有人建议这是一种很好的方法,但不确定最好的方法是什么。我有一个数据集,包括13个受试者和对照组的政策前观察结果--治疗组编码为“1”,对照组编码为“0”。我想使用Wald检验来检验sampole*time交互作用的显著性。谢谢
v8wbuo2f1#
包did将在输出摘要中报告Wald测试,如包作者所述:https://bcallaway11.github.io/did/articles/did-basics.html#an-example-with-real-data网站。这是总结底部报告的“平行趋势假设预检验的P值”。
did
# Example data data(mpdta) out1 <- att_gt(yname="lemp", tname="year", idname="countyreal", gname="first.treat", xformla=NULL, data=mpdta) summary(out1) #> #> Call: #> att_gt(yname = "lemp", tname = "year", idname = "countyreal", #> gname = "first.treat", xformla = NULL, data = mpdta) #> #> Reference: Callaway, Brantly and Pedro H.C. Sant'Anna. "Difference-in-Differences with Multiple Time Periods." Journal of Econometrics, Vol. 225, No. 2, pp. 200-230, 2021. <https://doi.org/10.1016/j.jeconom.2020.12.001>, <https://arxiv.org/abs/1803.09015> #> #> Group-Time Average Treatment Effects: #> Group Time ATT(g,t) Std. Error [95% Simult. Conf. Band] #> 2004 2004 -0.0105 0.0235 -0.0752 0.0542 #> 2004 2005 -0.0704 0.0307 -0.1549 0.0140 #> 2004 2006 -0.1373 0.0365 -0.2379 -0.0367 * #> 2004 2007 -0.1008 0.0383 -0.2062 0.0046 #> 2006 2004 0.0065 0.0236 -0.0585 0.0715 #> 2006 2005 -0.0028 0.0195 -0.0564 0.0509 #> 2006 2006 -0.0046 0.0185 -0.0556 0.0464 #> 2006 2007 -0.0412 0.0202 -0.0969 0.0145 #> 2007 2004 0.0305 0.0155 -0.0122 0.0733 #> 2007 2005 -0.0027 0.0158 -0.0462 0.0408 #> 2007 2006 -0.0311 0.0176 -0.0794 0.0173 #> 2007 2007 -0.0261 0.0167 -0.0720 0.0199 #> --- #> Signif. codes: `*' confidence band does not cover 0 #> #> P-value for pre-test of parallel trends assumption: 0.16812 #> Control Group: Never Treated, Anticipation Periods: 0 #> Estimation Method: Doubly Robust
1条答案
按热度按时间v8wbuo2f1#
包
did
将在输出摘要中报告Wald测试,如包作者所述:https://bcallaway11.github.io/did/articles/did-basics.html#an-example-with-real-data网站。这是总结底部报告的“平行趋势假设预检验的P值”。