Marginal Effects | Regression Analysis & Estimation Methods | Stata Tutorials Topic 39

Stata Tutorials Topic 39: Marginal Effects | Regression Analysis and Estimation Methods Using Stata
Hi, I am Bob. Welcome to the Stata course on regression analysis and estimation methods. In the last topic, we use the "margins" command to obtain the log hourly wage predictions at different explanatory variable values. We draw graphs to show how the average hourly wage changes with the explanatory variables using the "marginsplot" command. We used the margins and marginsplot commands to compute the predictions of the outcome variable and draw curves of the predictions against some explanatory variables. In addition to computing predictions, the margins and marginsplot commands are very useful in calculating the marginal effects of the explanatory variables. In this video, we will closely examine the "margins" command and produce the marginal effect of the explanatory variable on the outcome variable by using the "dydx" option. The marginal effect is also called the partial effect because we hold the other variables fixed.
Please download the BOB.dta dataset:
drive.google.com/file/d/13yxn...
#Stata #MarginalEffect #quadratics #interaction #margins #marginsplot #RegressionAnalysisandEstimationMethods #ols #tutorial
The Stata commands in this video:
*Marginal Effect of Schooling on Wage
regress lwage schooling age i.married i.female
margins, dydx(schooling)
*Marginal Effect Differs between Gender Groups
regress lwage c.schooling##i.female age i.married
margins, dydx(schooling) at(female==(0 1))
marginsplot
*Marginal Effect Changes with Schooling itself
regress lwage c.schooling##c.schooling i.female age i.married
margins, dydx(schooling) at(schooling==(6(1)17))
marginsplot
*Other Examples
regress lwage c.schooling##c.age i.married i.female
margins, dydx(schooling) at(age==(25(5)54))
marginsplot
regress lwage c.age##i.female schooling i.married
margins, dydx(female) at(age==(25(5)54))
marginsplot
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