Logit and probit regressions  (Page 7/9)

Stata commands

Place the data into the editor and then create a list of the independent variables. Now create a new variable equal to the log of income:

.generate linc = ln(hhinc)

[notice that 9 observations are eliminated.]

Create list of "extra" variables in order to shorten future commands:

. global extralist linc female white chronic adl sretire

Summarize the variables in order to check for obvious typos (output is suppressed):

.summarize ins retire $xlist $extralist

Estimate logit regression (output is shown in Figure 3):

.logit ins retire $xlist

Stata regression output.

Estimate and save results from several models (the Stata command "quietly" suppresses the output from the command):

. estimates store blogit

.quietly probit ins retire $xlist

.estimates store bprobit

.quietly regress ins retire $xlist

.estimates store bols

.quietly logit ins retire $list, vce(robust)

. estimates store blogitr

.quietly probit ins retire $xlist, vce(robust)

.estimates store bprobitr

.quietly regress ins retire $xlist, vce(robust)

.estimates store bolsr

We can create table for comparing the models (output is suppressed):

.estimates table blogit blogitr bprobit bprobitr bols bolsr, t stats(N ll) b(%8.4f) stfmt(%8.2f)

We now test for the presence of interaction variables:

.generate age2 = age*age

.generate agefem = age*fem

.generate agewhite = age*white

.generate agechronic = age*chronic

.global intlist age2 agefem agewhite agechronic

.quietly logit ins retire $xlist $intlist

.test $intlist

( 1) [ins]age2 = 0

( 2) [ins]agefem = 0

( 3) [ins]agewhite = 0

( 4) [ins]agechronic = 0

chi2( 4) = 7.45

Prob>chi2 = 0.1141

Likelihood ratio test

.quietly logit ins retire $xlist $intlist

.estimates store B

.quietly logit ins retire $xlist

.lrtest B

Likelihood-ratio test LR chi2(4) = 7.57

(Assumption: . nested in B) Prob>chi2 = 0.1088

Comparison with using the logistic command:

. logistic ins retire $xlist

The marginal effects at the mean will yield more useful results when the model is non-linear:

.quietly logit ins retire $xlist

.mfx

Let’s put the table comparing parameter estimates into a cleaned up table: