Home  /  Resources & support  /  FAQs  /  Obtaining robust standard errors for tobit
Note: This FAQ is for Stata 9 and older versions of Stata.

It is not relevant in Stata 10 and newer versions because the vce(robust) option may be specified with tobit.

In Stata 9, use the vce option with the tobit command to obtain estimates of the standard errors using nonparametric bootstrap or jackknife.

## How can I get robust standard errors for tobit?

 Title Obtaining robust standard errors for tobit Author James Hardin, StataCorp

The tobit command does not have the robust option; however, intreg does.

Since intreg is a generalization of cnreg (which is itself a generalization of tobit), you can use intreg to obtain the results that you want. Below is an illustrative example.

 . sysuse auto, clear

. tobit mpg price weight, ll(20)

Tobit regression                                  Number of obs   =         74
LR chi2(2)      =      61.85
Prob > chi2     =     0.0000
Log likelihood = -119.03328                       Pseudo R2       =     0.2062

------------------------------------------------------------------------------
mpg |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
price |   .0002594    .000289     0.90   0.372    -.0003166    .0008355
weight |  -.0084336   .0012104    -6.97   0.000    -.0108465   -.0060207
_cons |    43.4248   2.838216    15.30   0.000     37.76692    49.08268
-------------+----------------------------------------------------------------
/sigma |   4.578498   .5572115                      3.467717    5.689279
------------------------------------------------------------------------------
Obs. summary:         38  left-censored observations at mpg<=20
36     uncensored observations
0 right-censored observations

. gen mpg2 = mpg

. replace mpg2 = . if mpg<=20
(38 real changes made, 38 to missing)

. gen mpg3 = mpg

. replace mpg3 = 20 if mpg<=20

. intreg mpg2 mpg3 price weight

Fitting constant-only model:

Iteration 0:   log likelihood = -166.49203
Iteration 1:   log likelihood = -150.82635
Iteration 2:   log likelihood = -149.95827
Iteration 3:   log likelihood = -149.95786
Iteration 4:   log likelihood = -149.95786

Fitting full model:

Iteration 0:   log likelihood = -141.72401
Iteration 1:   log likelihood =  -119.8515
Iteration 2:   log likelihood = -119.03468
Iteration 3:   log likelihood = -119.03328
Iteration 4:   log likelihood = -119.03328

Interval regression                               Number of obs   =         74
LR chi2(2)      =      61.85
Log likelihood = -119.03328                       Prob > chi2     =     0.0000

------------------------------------------------------------------------------
|      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
price |   .0002594    .000289     0.90   0.369    -.0003069    .0008258
weight |  -.0084336   .0012104    -6.97   0.000     -.010806   -.0060612
_cons |    43.4248   2.838255    15.30   0.000     37.86193    48.98768
-------------+----------------------------------------------------------------
/lnsigma |   1.521371   .1217052    12.50   0.000     1.282833    1.759909
-------------+----------------------------------------------------------------
sigma |   4.578498   .5572271                      3.606844    5.811907
------------------------------------------------------------------------------

Observation summary:        38  left-censored observations
36     uncensored observations
0 right-censored observations
0       interval observations


The above example shows how the intreg command is used to obtain the same results as the tobit command. To obtain robust standard errors, you now only have to add the robust option to the intreg command above.