The name is absent



Stata Technical Bulletin

15


Summary information of the independent variables is obtained via the mean option.

. diest, mean fb(%9.3f) fm(%8.3f) fsd(%8.3f)

rep78 Repair Record 1978

I

Coef.

p>∣t∣

Mean

Std Dev

—

—

price Price

I

0.000

0.544

6165.257

2949.496

length Length (in.)

I

0.013

0.136

187.932

22.266

mpg Mileage (mpg)

I

0.067

0.018

21.297

5.786

foreign Car type

I

1.257

0.000

0.297

0.460

_cons

I

-0.930

0.645

Finally, we consider a more complicated example of a regression in which interaction effects are generated with xi.

. xi: regress rep78 price i.foreign*length i.foreign*mpg

!.foreign             Iforei-0-1   (naturally coded; Iforei-0 omitted)

i.foreign*length      IfXlen.#     (coded as above)

i.foreign*mpg         IfXmpg.#     (coded as above)

Source I        SS df MS                   Number of obs =      69

---------+-

—

—

F( 6,     62)

=    8.25

Model I

29.5749189

6 4.92915315

Prob > F

= 0.0000

Residual I

37.0627623

62 .597786488

R-Squared

= 0.4438

Adj R-Squared

= 0.3900

--——--———+—

—

Total I

66.6376812

68 .979965899

Root MSE

= .77317

—

rep78 I

Coef.

Std. Err.

t

p>∣t∣

[957. Conf.

—

Interval]

———————+—

—

price I

.0000155

.0000396

0.393

0.696

-.0000636

.0000947

Iforei-I I

-2.841789

4.520248

-0.629

0.532

-11.87763

6.194057

length I

.0125723

.0109983

1.143

0.257

-.0094131

.0345576

IfXlen.! I

.0263108

.0201156

1.308

0.196

-.0138997

.0665213

Iforei.l I

(dropped)

≡Pg I

.0857285

.0465508

1.842

0.070

-.0073252

.1787822

IfXmpg-I I

-.0163025

.0573871

-0.284

0.777

-.1310177

.0984126

-Cons I

-1.232495

2.959517

-0.416

0.679

-7.148485

4.683496

After issuing this command, one can easily obtain more readable output via the command

. diest, fb(%9.3f) fse(%9.3f)

rep78 Repair Record 1978

I

Coef.

Std. Err.

t

p>∣t∣

—

—

price Price

I

0.000

0.000

0.393

0.696

Iforei-I foreign==l

I

-2.842

4.520

-0.629

0.532

length Length (in.)

I

0.013

0.011

1.143

0.257

IfXlen-I (foreign==l)*length

I

0.026

0.020

1.308

0.196

Iforei-I foreign==l

I

(dropped)

mpg Mileage (mpg)

I

0.086

0.047

1.842

0.070

IfXmpg-I (foreign==l)*mpg

I

-0.016

0.057

-0.284

0.777

-Cons

I

-1.232

2.960

-0.416

0.679

sg61 Bivariate probit models

James W. Hardin, Stata Corp., FAX 1-409-696-4601, [email protected]

In this article, we discuss 3 different two-equation probit models that researchers may wish to estimate. They include

Bivariate probit regression for models where the two dependent variables depend on the same list of independent variables and
are correlated.

Seemingly unrelated two-equation probit regression for models where the two dependent variables may not depend on the
same list of independent variables, but are still correlated.

Nested probit regression for models where the outcome of one equation depends on the outcome of the other equation.

Interested readers may also find more information on these models in Greene (1993). Note also that although it is not
discussed in this article, these two commands could be used to extend Heckman-type models to consider two participation
equations.



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