The following statements show a subset of the Mroz (1987) data set. In these data, Hours
is the number of hours the wife worked outside the household in a given year, Yrs_Ed
is the years of education, and Yrs_Exp
is the years of work experience. A Tobit model will be fit to the hours worked with years of education and experience as
covariates.
By the nature of the data it is clear that there are a number of women who committed some positive number of hours to outside work ( is observed). There are also a number of women who did not work at all ( is observed). This gives us the following model:


where . The set of explanatory variables is denoted by .
title1 'Estimating a Tobit model'; data subset; input Hours Yrs_Ed Yrs_Exp @@; if Hours eq 0 then Lower=.; else Lower=Hours; datalines; 0 8 9 0 8 12 0 9 10 0 10 15 0 11 4 0 11 6 1000 12 1 1960 12 29 0 13 3 2100 13 36 3686 14 11 1920 14 38 0 15 14 1728 16 3 1568 16 19 1316 17 7 0 17 15 ;
/* Tobit Model */ proc qlim data=subset; model hours = yrs_ed yrs_exp; endogenous hours ~ censored(lb=0); run;
The output of the QLIM procedure is shown in Output 22.2.1.
Output 22.2.1: Tobit Analysis Results
Estimating a Tobit model 
Model Fit Summary  

Number of Endogenous Variables  1 
Endogenous Variable  Hours 
Number of Observations  17 
Log Likelihood  74.93700 
Maximum Absolute Gradient  1.18953E6 
Number of Iterations  23 
Optimization Method  QuasiNewton 
AIC  157.87400 
Schwarz Criterion  161.20685 
Parameter Estimates  

Parameter  DF  Estimate  Standard Error  t Value  Approx Pr > t 
Intercept  1  5598.295129  27.692220  202.16  <.0001 
Yrs_Ed  1  373.123254  53.988877  6.91  <.0001 
Yrs_Exp  1  63.336247  36.551299  1.73  0.0831 
_Sigma  1  1582.859635  390.076480  4.06  <.0001 
In the “Parameter Estimates” table there are four rows. The first three of these rows correspond to the vector estimate of the regression coefficients . The last one is called _Sigma, which corresponds to the estimate of the error variance .