|
This FAQ is for users of Stata 7. It is not relevant for Stata 8.
Stata 7: How do you compute marginal effects after ologit/oprobit/mlogit using mfx?
|
Title
|
|
Stata7: Marginal effects by example
|
|
Author
|
Ronna Cong, StataCorp
|
|
Date
|
February 2001; updated May 2001
|
The Stata 7 command mfx
numerically calculates the marginal effects or the elasticities and their
standard errors after estimation. mfx works after ologit,
oprobit, and mlogit. However, due to the multiple-outcome
feature of these three commands, one has to run mfx separately for each
outcome.
The marginal effect is defined as
d F(X)
----------
d X
The predict() option in mfx allows users to specify the form of
F(X), and the at() option allows users to specify the points (X) where
the marginal effects are to be evaluated.
Examples are presented under two sections: Section 1: Specifying the form of
F(X); and Section 2: Specifying the Xs.
Section 1: Specifying the form of F(X)
Exactly what mfx can calculate is determined by the previous estimation
command and by the predict() option. Users should check the
predict options for the estimation command before running mfx.
Examples are presented for biprobit,
heckman,
heckprob,
intreg,
mlogit,
ologit,
oprobit,
tobit,
treatreg,
xtintreg,
xtlogit,
xtprobit,
and xttobit.
The marginal effects for positive outcomes, Pr(depvar1=1, depvar2=1), are
. mfx compute, predict(p11)
The marginal effects for Pr(depvar1=1, depvar2=0) are
. mfx compute, predict(p10)
The marginal effects for Pr(depvar1=0, depvar2=1) are
. mfx compute, predict(p01)
The marginal effects for Pr(depvar1=0, depvar2=0) are
. mfx compute, predict(p00)
The marginal effects for the marginal probability of outcome 1, Pr(depvar1=1),
are
. mfx compute, predict(pmarg1)
The marginal effects for the marginal probability of outcome 2, Pr(depvar2=1),
are
. mfx compute, predict(pmarg2)
The marginal effects for the conditional probability of outcome 1 given
outcome 2, Pr(depvar1=1 | depvar2=1), are
. mfx compute, predict(pcond1)
The marginal effects for the conditional probability of outcome 2 given
outcome 1, Pr(depvar2=1 | depvar1=1), are
. mfx compute, predict(pcond2)
The marginal effects for the expected value of the dependent variable
conditional on being observed, E(y | y observed), are
. mfx compute, predict(ycond)
The marginal effects for the probability of the dependent variable being
observed, Pr(y observed), are
. mfx compute, predict(psel)
The marginal effects for the probability of a positive outcome, Pr(depvar=1),
are
. mfx compute, predict(pmargin)
The marginal effects for the probability of a positive outcome given the
dependent variable being observed, Pr(depvar=1 | depvar_s=1), are
. mfx compute, predict(pcond)
The marginal effects for the probability of the dependent variable being
observed, Pr(depvar_s=1), are
. mfx compute, predict(psel)
The marginal effects for Pr(depvar=1, depvar_s=1) are
. mfx compute, predict(p11)
The marginal effects for Pr(depvar=1, depvar_s=0) are
. mfx compute, predict(p10)
The marginal effects for Pr(depvar=0, depvar_s=1) are
. mfx compute, predict(p01)
The marginal effects for Pr(depvar=0, depvar_s=0) are
. mfx compute, predict(p00)
The marginal effects for the probability of being uncensored are
. mfx compute, predict(p(a,b))
where a is the lower limit for left censoring and b is the upper limit for
right censoring.
The marginal effects for the expected value of the dependent variable
conditional on being uncensored, E(y | a<y<b), are
. mfx compute, predict(e(a,b))
where a is the lower limit for left censoring and b is the upper limit for
right censoring.
The marginal effects for the unconditional expected value of the dependent
variable, E(y*), where y* = max(a, min(y,b)), are
. mfx compute, predict(ys(a, b))
where a is the lower limit for left censoring and b is the upper limit for
right censoring.
Due to the multiple-outcome feature of these three commands, mfx must
be run separately for each outcome.
Run the estimation command first:
. use auto, clear
. mlogit rep78 mpg
or
. ologit rep78 mpg
or
. oprobit rep78 mpg
The marginal effects for the probability of outcome 1, Pr(y=1), are
. mfx compute, predict(outcome(1))
The marginal effects for the probability of outcome 2, Pr(y=2), are
. mfx compute, predict(outcome(2))
and so forth.
The marginal effects for the expected value of y conditional on being treated,
E(y | treatment = 1), are
. mfx compute, predict(yctrt)
The marginal effects for the expected value of y conditional on being untreated,
E(y | treatment =0), are
. mfx compute, predict(ycntrt)
The marginal effects for the probability of being treated, Pr(treatment=1), are
. mfx compute, predict(ptrt)
The marginal effects for the probability of being uncensored are
. mfx compute, predict(pr0(a, b))
where a is the lower limit for left censoring and b is the upper limit for
right censoring.
The marginal effects for the expected value of y conditional on being
uncensored are
. mfx compute, predict(e0(a, b))
where a is the lower limit for left censoring and b is the upper limit for
right censoring.
The marginal effects for the unconditional expected value of y are
. mfx compute, predict(ys(a, b))
where a is the lower limit for left censoring and b is the upper limit for
right censoring.
The marginal effects for predicted probability after the random-effects model
are
. mfx compute, predict(pu0)
The marginal effects for the predicted probability, taking into account
offset() after the population-averaged model, are
. mfx compute, predict(mu)
The marginal effects for predicted probability, ignoring offset() after the
population-averaged model, are
. mfx compute, predict(rate)
Section 2: Specifying the Xs
By default, mfx calculates the marginal effects at the means of the
independent variables.
To calculate the marginal effects at the medians, type
. mfx compute, at(median)
To calculate the marginal effects at zeros, type
. mfx compute, at(zero)
To calculate the marginal effects at mpg=20 and zeros for other independent
variables, type
. mfx compute, at(zero mpg=20)
|
FAQs
What's new?
Statistics
Data management
Graphics
Programming Stata
Mata
Resources
Internet capabilities
Stata for Windows
Stata for Unix
Stata for Mac
Technical support
|
