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How to obtain marginal effects and their standard errors?
In Stata 11, the margins command replaced mfx . I am using a model with interactions. How can I obtain marginal effects and their standard errors? The marginal effect of an independent variable is the derivative (that is, the slope) of a given function of the covariates and coefficients of the preceding estimation.
How to plot the marginal effects of an interaction?
To plot the marginal effects of interaction terms, simply specify these terms in the terms -argument. Since the terms -argument accepts up to four model terms, you can also compute marginal effects for a 3-way-interaction or 4-way-interaction.
How to calculate the marginal effect of X?
I know how to calculate the marginal effect of X in both cases, but not how to calculate its standard error. Please, any hints on how to do this, both theoretically or in R code, may be very helpfull. If you treat Z as non-random variable, then the marginal effect is b 1 + b 3 ⋅ Z, a function of Z.
How to make marginal effect plots in Stata?
Here you can find Stata code for producing a marginal effect plot for one of the interacting variables, in this case X, based on three different types of multiplicative interaction models. It should be easy to adapt this code to deal with other types of interaction models.
Is there a margins command in Stata 11?
Note: This FAQ is for Stata 10 and older versions of Stata. In Stata 11, the margins command replaced mfx . I am using a model with interactions. How can I obtain marginal effects and their standard errors?
How is the marginal effect of a dummy variable obtained?
The marginal effect for a dummy variable is not obtained by differentiation but as a difference of the predicted value at 1 and the predicted value at 0. Here is an example of a logit model with an interaction, where one variable is a dummy.
How is the marginal effect of an independent variable evaluated?
The marginal effect of an independent variable is the derivative (that is, the slope) of a given function of the covariates and coefficients of the preceding estimation. The derivative is evaluated at a point that is usually, and by default, the means of the covariates.