CQLIM Procedure

This example illustrates the use of the CQLIM procedure. The data were originally published by Mroz (1987), and the following DATA steps load a subset of the data. The assumption here is that your libref is named mylib, but you can substitute any appropriately defined libref.

title1 'Estimating a Tobit Model';

data subset;
   input Hours Yrs_Ed Yrs_Exp @@;
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
;

data mylib.subset;
   set subset;
run;

In these data, Hours is the number of hours that a wife worked outside the household in a particular year, Yrs_Ed is years of education, and Yrs_Exp is years of work experience.

From 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 outside the household at all ( is observed). This yields the following model,

where and denotes the set of explanatory variables. The following statements fit a Tobit model to the number of hours worked, with years of education and years of work experience as covariates:

/*-- Tobit Model --*/
proc cqlim data=mylib.subset;
   model hours = yrs_ed yrs_exp;
   endogenous hours ~ censored(lb=0);
run;

The output of the CQLIM procedure is shown in Figure 1.

The "Parameter Estimates" table contains four rows. The first three rows correspond to the vector estimate of the regression coefficients . The last row is called _Sigma, which corresponds to the estimate of the error variance .