The QLIM Procedure

Example 22.5 Sample Selection Model with Truncation and Censoring

In this example the data are generated such that the selection variable is discrete and the variable Y is truncated from below by zero. The program follows, with the results shown in Output 22.5.1:

data trunc;
   keep z y x1 x2;
   do i = 1 to 500;
      x1 = rannor( 19283 );
      x2 = rannor( 19283 );
      u1 = rannor( 19283 );
      u2 = rannor( 19283 );
      zl = 1 + 2 * x1 + 3 * x2 + u1;
      y = 3 + 4 * x1 - 2 * x2 + u1*.2 + u2;
      if ( zl > 0 ) then z = 1;
      else z = 0;
      if y>=0 then output;
   end;
run;
/*-- Sample Selection with Truncation --*/
proc qlim data=trunc method=qn;
   model z = x1 x2 / discrete;
   model y = x1 x2 / select(z=1) truncated(lb=0);
run;

Output 22.5.1: Sample Selection with Truncation

Binary Data

The QLIM Procedure

Model Fit Summary
Number of Endogenous Variables 2
Endogenous Variable z y
Number of Observations 379
Log Likelihood -344.10722
Maximum Absolute Gradient 4.95535E-6
Number of Iterations 17
Optimization Method Quasi-Newton
AIC 704.21444
Schwarz Criterion 735.71473

Parameter Estimates
Parameter DF Estimate Standard
Error
t Value Approx
Pr > |t|
y.Intercept 1 3.014158 0.128548 23.45 <.0001
y.x1 1 3.995671 0.099599 40.12 <.0001
y.x2 1 -1.972697 0.096385 -20.47 <.0001
_Sigma.y 1 0.923428 0.047233 19.55 <.0001
z.Intercept 1 0.949444 0.190265 4.99 <.0001
z.x1 1 2.163928 0.288384 7.50 <.0001
z.x2 1 3.134213 0.379251 8.26 <.0001
_Rho 1 0.494356 0.176542 2.80 0.0051



In the following statements the data are generated such that the selection variable is discrete and the variable $Y$ is censored from below by zero. The results are shown in Output 22.5.2.

data cens;
   keep z y x1 x2;
   do i = 1 to 500;
      x1 = rannor( 19283 );
      x2 = rannor( 19283 );
      u1 = rannor( 19283 );
      u2 = rannor( 19283 );
      zl = 1 + 2 * x1 + 3 * x2 + u1;
      yl = 3 + 4 * x1 - 2 * x2 + u1*.2 + u2;
      if ( zl > 0 ) then z = 1;
      else               z = 0;
      if ( yl > 0 ) then y = yl;
      else               y = 0;
      output;
   end;
run;
/*-- Sample Selection with Censoring --*/
proc qlim data=cens method=qn;
   model z = x1 x2 / discrete;
   model y = x1 x2 / select(z=1) censored(lb=0);
run;

Output 22.5.2: Sample Selection with Censoring

Binary Data

The QLIM Procedure

Model Fit Summary
Number of Endogenous Variables 2
Endogenous Variable z y
Number of Observations 500
Log Likelihood -399.78508
Maximum Absolute Gradient 2.30443E-6
Number of Iterations 19
Optimization Method Quasi-Newton
AIC 815.57015
Schwarz Criterion 849.28702

Parameter Estimates
Parameter DF Estimate Standard
Error
t Value Approx
Pr > |t|
y.Intercept 1 3.074276 0.111617 27.54 <.0001
y.x1 1 3.963619 0.085796 46.20 <.0001
y.x2 1 -2.023548 0.088714 -22.81 <.0001
_Sigma.y 1 0.920860 0.043278 21.28 <.0001
z.Intercept 1 1.013610 0.154081 6.58 <.0001
z.x1 1 2.256922 0.255999 8.82 <.0001
z.x2 1 3.302692 0.342168 9.65 <.0001
_Rho 1 0.350776 0.197093 1.78 0.0751