尽管在上面的输出中警告我们不应该比较事件研究估计值,但我们仍然可以使用 event_plot后面 命令绘制这些估计值,如下所示: event_plot命令可以如下安装 ssc install event_plot, replace event_plot e(estimates)#e(variances), default_look /// graph_opt(xtitle("Periods since the event") ytitle("Average cau...
event_plot model1 model2 model3, stub_lag(post# post# fpost#) stub_lead(pre_# pre_# fpre_#) plottype(connected ) ciplottype(rcap) together noautolegend graph_opt(xtitle("Period", size(middle))ytitle("Average Treatment Effect", size(middle)) xlabel(-7(1)7,)legend(order( 1 "en...
eventdd``xtreg``reghdfe``event_plot``eventdd``eventdd``matsort 平行趋势检验代码汇总 ***EventStudyPlots*** genrel_time=year-_nfd *install"eventdd"and"matsort" 选择方法为 "xtreg". eventddasmrspcincasmrhcasesi.year,/// timevar(rel_time)method(fe,cluster(stfips))/// nolinegraph_op(...
agg(event):设置聚合方法,这里设置为事件 并保存相应的系数矩阵C_S,用于之后的画图。 4)交互加权 用Sun and Abraham(2021)的eventstudyinteract实现交互加权(interaction weighted)估计量,构造动态处理效果的点估计置信区间。 在前期准备中,首先需要构建虚拟变量lastcohort,将控制组作为末端序列: suyearloca=r(min)loc...
clear all timer clear * 设定 1500 个观察值及其他暂元 set seed 10 global T = 15 global I = 100 global pre 5 global post 8 global ep event_plot global g0 "default_look" global g1 xla(-$pre (1) $post) // global g1 xla(-5(1)5) global g2 xt("Periods since the event") glob...
event_plot, default_lookgraph_opt(xtitle("相对处理时点")ytitle("处理效应")xlabel(-5(1)5))该方法通过分时处理队列计算ATT，特别处理了处理时点与协变量相关的偏误。注意需检查平行趋势假设，建议配合bacondecomp命令进行分解诊断。方法二：Sun& Abraham (2021)事件研究法 构造动态处理效应模型：eventstudy...
S458835 SPNORM: Stata module to plot Shaded Percentiles of Normal Distributions by Nicola Orsini S458834 R_TO_D: Stata module for converting Pearson’s r to Cohen’s d by Ariel Linden S458833 EVENTSTUDYWEIGHTS: Stata module to estimate the implied weights on the cohort-specific average treatme...
S458835 SPNORM: Stata module to plot Shaded Percentiles of Normal Distributions byNicola Orsini S458834 R_TO_D: Stata module for converting Pearson's r to Cohen's d byAriel Linden S458833 EVENTSTUDYWEIGHTS: Stata module to estimate the implied weights on the cohort-specific average treatment ...
Avoiding the eyeballing fallacy: Visualizing statistical differences between estimates using the pheatplot command E. Brini, S. T. Borgen, and N. T. Borgen xtevent: Estimation and visualization in the linear panel event-study design S. Freyaldenhoven, C. B. Hansen, J. Pérez Pérez, J. M...
(gaps). You can estimate and plot the probability of survival over time. Or model survival as a function of covariates using Cox, Weibull, lognormal, and other regression models. Predict hazard ratios, mean survival time, and survival probabilities. Do you have groups of individuals in your ...