vce(clustercity_id)matC=e(b_iw)matast_matrix("A",sqrt(st_matrix("e(V_iw)")))matC=C\AmatlistC*eventstudyinteractlnemissionsg_*g0-g8,///cohort(first_year)control_cohort(never_treat)///covariates($controlslnworkers)///absorb(i.city_idi.year)vce(clustercity_id)matD=e(b_iw)matast...
xtevent ln_w age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure , pol(union) w(3) cluster(idcode) xteventplot xteventplot, noci
t bands estimates_ols <- EventStudy( estimator = "OLS", data = example_data, # Use package sample data outcomevar = "y_smooth_m", policyvar = "z", idvar = "id", timevar = "t", controls = "x_r", pre = 0, post = 4 ) plt <- EventStudyPlot(estimates = estimates_ols) ...
The HonestDiD R package implements the tools for robust inference and sensitivity analysis for differences-in-differences and event study designs developed in Rambachan and Roth (2022). There is also an HonestDiD Stata package, and a Shiny app developed by Chengcheng Fang. Background The robust...
We introduce statistical programmes for implementation in Stata. Results/case study Visualisations of AEs in the COVID-19 trial communicated a risk profile for remdesivir which differed from the main message in the published authors' conclusion. In the Parkinson's disease trial of GDNF, the ...
It allows for compatibility between the study analysis model and the imputation model. This method is available in both Stata and R software. Keogh and Morris [5] have adapted SMC-FCS to allow for the presence of time-varying effects and proposed an algorithm to allow for model selection ...
All statistical analyses were computed with Stata/SE version 16.1 (Stata Corp, College Station, Tex). 2.6.1. Sensitivity and subgroup analysis Sensitivity analysis was used to investigate significant heterogeneity and included the leave-one-out method (24) to identify the influence of a single ...
An event time is also usually of interest, for example time of death or study drop-out. It has been repeatedly shown elsewhere that if the longitudinal and event-time outcomes are correlated, then modelling the two outcome processes separately, for example using linear mixed models and Cox ...
This is a Stata package for Borusyak, Jaravel, and Spiess (2023), "Revisiting Event Study Designs: Robust and Efficient Estimation"The package includes:did_imputation command: for estimating causal effects & testing for pre-trends with the imputation method of Borusyak et al. event_plot command...
(rel_varlist1, Symbol("g"*string(i))) end control_cohort1 = :never_treat cohort1 = :first_treat m5 = eventreg(df, formula1, rel_varlist1, control_cohort1, cohort1) plot2 = mycoefplot(m5) Performance EventStudyInteracts.jl is 3.69 times faster than Stata when controlling only for ...