Difference-in-Difference-in-Differences Method (DDD) | Estimation Methods | Stata Tutorials Topic 43 

Stata Tutorials Topic 43: Difference-in-Difference-in-Differences Method (DDD) | Regression Analysis and Estimation Methods Using Stata
Hi, I am Bob. Welcome to the Stata course on regression analysis and estimation methods. Today, we will continue our discussion about the difference-in-differences method. The difference-in-differences design is a powerful identification strategy for causal effects analysis. It can alleviate the omitted variable bias and help us find the causal effect of the treatment on the outcome variable. It depends on a crucial assumption, the parallel trends assumption. That is, the treatment group and the control group are similar in every aspect except that the control group did not receive the treatment. It is equivalent to saying that factors other than the treatment affect the outcomes of the control and treatment groups in the same way. In other words, the outcome variables of the treatment group and the control group have the same trend if neither of them experiences the intervention. If the parallel trends assumption is violated, we have two solutions. One is to add control variables to the model to account for the different trends between the treatment and control groups. Another method is to use the difference-in-difference-in-differences estimation (DDD). Let's show you these two approaches using an example in Stata.
Please download the INJURY.dta dataset for this topic:
drive.google.com/file/d/1yJrn...
#Stata #DDD #tutorial #DID #regress #interactionterm #RegressionAnalysisandEstimationMethods #OLS #ATE #controlgroup #treatmentgroup #TreatmentEffect #Difference-in-Differences #tutorial
The Stata commands in this video:
*DID
regress ldurat i.afchnge##i.highearn if ky==1
estimates store DID
*DID with Controls
describe
regress ldurat i.afchnge##i.highearn male married age i.indust i.injtype if ky==1
estimates store DID2
esttab DID DID2, keep(1.afchnge 1.highearn 1.afchnge#1.highearn) star(* 0.1 * 0.05 ** 0.01) stat(N) mtitle se
*DDD
regress ldurat i.afchnge##i.highearn##i.ky
*Confirm the control state
regress ldurat i.afchnge##i.highearn if mi==1
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