* DOUBLE SIDED TOBIT MODEL - UPPER AND LOWER LIMITS. IN THIS CASE, DEPVAR IS BOUNDED BETWEEN 0 AND 4;
* Data is example from Greene;
*   http://shazam.econ.ubc.ca/student/greene/;
* Check reference above to verify results in SHAZAM Econometrics Software;
* I don't believe SAS automatically produces predicted values, so you have to do it this way:;
* As you can see from the t-stats, this model is not very predictive, but is for illustration;
* purposes in that the results are published in Green so we can cross validate yhat calculations;
data mydata;
 input Y z2 z3 z5 z7 z8;
cards;
0	37	10	3	7	4
0	27	4	4	6	4
0	32	15	1	1	4
0	57	15	5	6	5
0	22	0.75	2	6	3
0	32	1.5	2	5	5
0	22	0.75	2	1	3
0	57	15	2	4	4
0	32	15	4	1	2
0	22	1.5	4	4	5
0	37	15	2	7	2
0	27	4	4	6	4
0	47	15	5	6	4
0	22	1.5	2	5	4
0	27	4	4	5	4
0	37	15	1	5	5
0	37	15	2	4	3
0	22	0.75	3	5	4
0	22	1.5	2	5	5
0	27	10	2	1	5
0	22	1.5	2	5	5
0	22	1.5	2	5	5
0	27	10	4	5	4
0	32	10	3	1	5
0	37	4	2	6	4
0	22	1.5	2	5	5
0	27	7	4	1	5
0	42	15	5	6	4
0	27	4	3	5	5
0	27	4	3	5	4
0	42	15	4	6	3
0	22	1.5	3	5	5
0	27	0.417	4	6	4
0	42	15	5	5	4
0	32	4	1	6	4
0	22	1.5	4	5	3
0	42	15	3	1	4
0	22	4	4	5	5
0	22	1.5	1	3	5
0	22	0.75	3	1	5
0	32	10	5	6	5
0	52	15	5	6	3
0	22	0.417	5	1	4
0	27	4	2	6	1
0	32	7	5	5	3
0	22	4	3	5	5
0	27	7	4	6	5
0	42	15	2	5	4
0	27	1.5	4	3	5
0	42	15	2	6	4
0	22	0.75	5	3	5
0	32	7	2	6	4
0	27	4	5	6	5
0	27	10	4	6	4
0	22	4	1	5	5
0	37	15	4	3	1
0	22	1.5	5	4	4
0	37	15	4	1	5
0	27	0.75	4	5	4
0	32	10	4	6	4
0	47	15	5	7	2
0	37	10	3	6	4
0	22	0.75	2	5	5
0	27	4	2	4	5
0	32	7	4	6	4
0	42	15	2	3	5
0	37	10	4	6	4
0	47	15	3	6	5
0	22	1.5	5	5	5
0	27	1.5	2	6	4
0	27	4	3	5	5
0	32	10	5	4	5
0	22	0.125	2	5	5
0	47	15	4	4	3
0	32	15	1	5	5
0	27	7	4	5	5
0	22	1.5	3	5	5
0	27	4	3	6	5
0	22	1.5	3	5	5
0	57	15	2	7	2
0	17.5	1.5	3	6	5
0	57	15	4	6	5
0	22	0.75	2	3	4
0	42	4	4	3	3
0	22	1.5	4	1	5
0	22	0.417	1	6	4
0	32	15	4	5	5
0	27	1.5	3	5	2
0	22	1.5	3	1	5
0	37	15	3	1	4
0	32	15	4	3	4
0	37	10	2	5	3
0	37	10	4	5	4
0	57	15	5	5	3
0	27	0.417	1	3	4
0	42	15	5	1	5
0	57	15	3	6	1
0	37	10	1	6	4
0	37	15	3	5	5
0	37	15	4	6	5
0	27	10	5	1	5
0	37	10	2	6	4
0	22	0.125	4	4	5
0	57	15	5	6	5
0	37	15	4	6	4
0	22	4	4	6	4
0	27	7	4	5	4
0	57	15	4	5	4
0	32	15	3	6	3
0	22	1.5	2	5	4
0	32	7	4	1	5
0	37	15	4	6	5
0	32	1.5	5	5	5
0	42	10	5	7	4
0	27	7	3	5	4
0	37	15	4	6	5
0	37	15	4	3	2
0	32	10	5	6	4
0	22	0.75	4	1	5
0	27	7	4	2	4
0	27	7	2	2	5
0	42	15	5	5	4
0	42	15	4	5	3
0	27	7	2	1	2
0	22	1.5	3	5	5
0	37	15	5	6	5
0	22	0.125	2	4	5
0	27	1.5	4	5	5
0	32	1.5	2	6	5
0	27	1.5	2	6	5
0	27	10	4	1	3
0	42	15	4	6	5
0	27	1.5	2	6	5
0	27	4	2	6	3
0	32	10	3	5	3
0	32	15	3	5	4
0	22	0.75	2	6	5
0	37	15	2	1	4
0	27	4	4	5	5
0	27	4	1	5	4
0	27	10	2	1	4
0	32	15	5	6	4
0	27	7	5	5	3
0	52	15	2	5	4
0	27	4	3	6	3
0	37	4	1	5	4
0	27	4	4	5	4
0	52	15	5	1	3
0	57	15	4	6	4
0	27	7	1	5	4
0	37	7	4	6	3
0	22	0.75	2	4	3
0	32	4	2	5	3
0	37	15	4	6	3
0	22	0.75	2	4	3
0	42	15	4	6	3
0	52	15	5	1	1
0	37	15	4	1	2
0	27	7	4	5	3
0	32	4	2	5	5
0	27	4	2	6	5
0	27	4	2	5	5
0	37	15	5	6	5
0	47	15	5	5	4
0	32	10	3	1	4
0	27	1.5	4	1	2
0	57	15	2	5	2
0	22	1.5	4	5	4
0	42	15	3	3	4
0	57	15	4	2	2
0	57	15	4	6	5
0	22	0.125	4	4	5
0	32	10	4	1	5
0	42	15	3	5	4
0	27	1.5	2	6	5
0	32	0.125	2	5	2
0	27	4	3	5	4
0	27	10	2	1	4
0	32	7	4	1	3
0	37	15	4	5	4
0	42	15	5	6	2
0	32	1.5	4	6	5
0	32	4	3	5	3
0	37	7	4	5	5
0	22	0.417	3	3	5
0	27	7	4	1	5
0	27	0.75	3	5	5
0	27	4	2	5	5
0	32	10	4	4	5
0	32	15	1	5	5
0	22	0.75	3	4	5
0	27	7	4	1	4
0	27	0.417	4	5	4
0	37	15	4	5	4
0	37	15	2	1	3
0	22	4	1	5	4
0	37	15	4	5	3
0	22	1.5	2	4	5
0	52	15	4	6	2
0	22	1.5	4	5	5
0	32	4	5	3	5
0	32	4	2	3	5
0	22	1.5	3	6	5
0	27	0.75	2	3	3
0	22	7	2	5	2
0	27	0.75	2	5	3
0	37	15	4	1	2
0	22	1.5	1	1	5
0	37	10	2	4	4
0	37	15	4	5	3
0	42	15	3	3	3
0	22	4	2	5	5
0	52	7	2	6	2
0	27	0.75	2	5	5
0	27	4	2	4	5
0	42	1.5	5	6	5
0	22	1.5	4	6	5
0	22	4	4	5	3
0	22	4	1	5	4
0	37	15	5	4	5
0	37	10	3	6	3
0	42	15	4	6	5
0	47	15	4	5	5
0	22	1.5	4	5	4
0	32	10	3	1	4
0	22	7	1	3	5
0	32	10	4	5	4
0	27	1.5	2	2	4
0	37	15	4	5	5
0	42	4	3	4	5
0	37	15	5	5	4
0	32	7	4	5	5
0	42	15	4	6	5
0	27	4	4	6	4
0	22	0.75	4	6	5
0	27	4	4	5	3
0	22	0.75	5	1	5
0	52	15	5	5	5
0	32	10	3	5	5
0	37	15	4	4	4
0	32	7	2	5	4
0	42	15	3	1	4
0	32	15	1	5	5
0	27	4	3	5	5
0	32	15	4	3	4
0	22	0.75	3	2	4
0	22	1.5	3	5	3
0	42	15	4	3	5
0	52	15	3	5	4
0	37	15	5	6	4
0	47	15	4	2	3
0	57	15	2	6	4
0	32	7	4	5	5
0	27	7	4	1	4
0	22	1.5	1	6	5
0	22	4	3	1	4
0	22	1.5	2	1	5
0	42	15	2	6	4
0	57	15	4	2	4
0	27	7	2	1	5
0	22	4	3	1	5
0	37	15	4	5	3
0	32	7	1	6	4
0	22	1.5	2	5	5
0	22	1.5	3	1	3
0	52	15	2	5	5
0	37	15	2	1	1
0	32	10	2	5	5
0	42	15	4	4	5
0	27	4	3	4	5
0	37	15	4	6	5
0	27	1.5	3	5	5
0	22	0.125	2	6	3
0	32	10	2	6	3
0	27	4	4	5	4
0	27	7	2	5	1
0	32	4	5	6	3
0	37	15	2	5	5
0	47	15	4	6	4
0	27	1.5	1	5	5
0	37	15	4	6	4
0	32	15	4	1	4
0	32	7	4	5	4
0	42	15	3	1	3
0	27	7	3	1	4
0	27	1.5	3	4	2
0	22	1.5	3	3	5
0	27	4	3	4	2
0	27	7	3	1	2
0	37	15	2	5	4
0	37	7	3	4	4
0	22	1.5	2	5	5
0	37	15	5	5	4
0	22	1.5	4	5	3
0	32	10	4	1	5
0	27	4	2	5	3
0	22	0.417	4	5	5
0	27	4	2	5	5
0	37	15	4	5	3
0	37	10	5	7	4
0	27	7	2	4	2
0	32	4	2	5	5
0	32	4	2	6	4
0	22	1.5	3	4	5
0	22	4	4	3	4
0	17.5	0.75	2	5	4
0	32	10	4	4	5
0	32	0.75	5	3	3
0	37	15	4	5	3
0	32	4	3	4	5
0	27	1.5	2	3	2
0	22	7	4	1	5
0	47	15	5	6	5
0	27	4	1	4	4
0	37	15	5	1	3
0	42	4	4	5	5
0	32	4	2	1	5
0	52	15	2	7	4
0	22	1.5	2	1	4
0	52	15	4	2	4
0	22	0.417	3	1	5
0	22	1.5	2	5	5
0	27	4	4	6	4
0	32	15	4	1	5
0	27	1.5	2	3	5
0	32	4	1	6	5
0	37	15	3	6	4
0	32	10	2	6	5
0	32	10	5	5	5
0	37	1.5	4	5	3
0	32	1.5	2	4	4
0	32	10	4	1	4
0	47	15	4	5	4
0	27	10	5	1	5
0	27	4	3	4	5
0	37	15	4	4	2
0	27	0.75	4	5	5
0	37	15	4	1	5
0	32	15	3	1	5
0	27	10	2	1	5
0	27	7	2	6	5
0	37	15	2	1	3
0	27	1.5	2	4	4
0	22	0.75	2	1	5
0	22	4	4	2	4
0	42	0.125	4	6	4
0	27	1.5	4	6	5
0	27	7	3	6	3
0	52	15	4	1	3
0	27	1.5	5	5	2
0	27	1.5	2	5	5
0	27	1.5	3	5	5
0	22	0.125	5	4	4
0	27	4	4	1	5
0	27	4	4	1	5
0	47	15	2	5	5
0	32	15	3	5	3
0	42	7	2	5	5
0	22	0.75	4	6	4
0	27	0.125	3	6	5
0	32	10	3	6	5
0	22	0.417	5	4	5
0	47	15	5	1	4
0	32	10	3	1	5
0	57	15	4	5	5
0	27	4	3	6	5
0	32	7	4	1	5
0	37	10	4	1	5
0	32	10	1	1	4
0	22	4	3	1	4
0	27	7	4	3	2
0	57	15	5	5	2
0	32	7	2	5	5
0	27	1.5	4	1	3
0	22	1.5	4	5	5
0	22	1.5	4	5	4
0	32	7	3	1	5
0	47	15	3	5	4
0	22	0.75	3	1	5
0	22	1.5	2	5	5
0	27	4	1	5	5
0	52	15	4	5	5
0	32	10	4	6	5
0	47	15	4	6	4
0	27	7	2	1	2
0	22	1.5	4	4	5
0	32	10	2	5	4
0	22	0.75	2	5	4
0	22	1.5	2	5	5
0	42	15	3	6	4
0	27	7	5	4	5
0	42	15	4	4	4
0	57	15	3	5	2
0	42	15	3	6	2
0	32	7	2	1	2
0	22	4	5	4	5
0	22	1.5	1	6	5
0	22	0.75	1	4	5
0	32	15	4	1	5
0	22	1.5	2	5	3
0	27	4	5	2	5
0	27	4	4	1	5
0	42	15	5	5	4
0	32	1.5	2	7	3
0	57	15	4	3	1
0	37	7	4	5	5
0	52	15	2	5	4
0	47	15	4	6	5
0	27	7	2	5	4
0	27	7	4	5	5
0	22	4	2	3	3
0	37	7	2	6	5
0	27	7	4	4	3
0	42	10	4	6	4
0	22	1.5	3	1	5
0	22	4	2	1	3
0	57	15	4	6	5
0	37	15	4	4	3
0	27	7	3	5	5
0	17.5	10	4	4	5
0	22	4	4	5	5
0	27	4	2	1	4
0	37	15	2	5	1
0	22	1.5	5	1	4
0	27	7	2	5	4
0	27	4	4	5	5
0	22	0.125	1	3	5
0	27	7	4	1	4
0	32	15	5	5	3
0	32	10	4	5	4
0	32	15	2	3	4
0	22	1.5	3	5	5
0	27	4	4	4	4
0	52	15	5	1	5
0	27	7	2	1	2
0	27	7	3	1	4
0	42	15	2	1	4
0	42	15	4	5	4
0	27	7	4	3	3
0	27	7	2	6	2
0	42	15	3	3	3
0	27	4	3	3	5
0	27	7	3	1	4
0	22	1.5	2	4	5
0	27	4	4	1	4
0	22	4	4	5	5
0	22	1.5	2	4	5
0	47	15	4	5	4
0	37	10	2	6	2
0	37	15	3	5	4
0	27	4	2	1	4
3	27	1.5	3	4	4
3	27	4	3	1	5
4	37	15	5	6	2
4	32	10	3	5	2
1	22	0.125	4	5	5
1	22	1.5	2	1	5
4	37	15	4	5	2
4	22	1.5	2	3	4
2	37	15	2	6	4
3	32	15	4	3	2
1	37	15	4	4	2
4	42	15	3	1	4
4	42	15	5	4	1
4	37	10	2	6	2
4	32	15	3	1	2
3	27	4	1	6	5
4	37	10	2	7	3
4	27	4	3	5	5
1	42	15	4	5	5
1	47	15	5	4	5
4	27	4	3	5	4
1	27	7	5	1	4
4	27	1.5	3	5	4
4	27	7	4	6	2
3	42	15	4	5	4
4	27	10	4	7	3
1	27	1.5	2	5	2
1	32	4	4	6	4
1	27	7	3	1	3
3	32	10	4	1	4
3	27	4	2	7	2
1	17.5	0.75	5	4	5
1	32	10	4	1	5
4	32	7	2	6	4
4	37	15	2	6	4
4	37	10	1	5	3
4	32	10	2	5	5
4	52	15	2	6	4
4	42	15	1	1	3
1	52	15	2	6	3
2	37	15	3	6	5
4	22	4	3	3	4
4	27	7	1	6	2
1	27	4	3	5	5
4	47	15	4	6	5
4	42	15	4	1	1
4	27	4	3	3	4
4	32	7	4	4	5
1	32	0.417	3	3	4
3	47	15	5	5	4
4	37	15	2	5	4
4	22	4	2	6	4
1	27	4	2	4	5
4	52	15	5	1	3
1	27	4	3	3	3
1	27	10	4	1	4
1	32	7	3	7	4
4	32	7	2	4	1
3	22	1.5	1	3	2
4	22	4	3	6	4
4	42	15	4	6	4
2	57	15	1	5	4
4	32	4	3	5	2
1	27	4	1	4	4
4	32	7	4	1	4
2	57	15	1	4	4
4	42	15	4	5	2
4	37	10	1	5	3
3	42	15	3	6	1
1	52	15	3	4	4
2	27	7	3	5	3
4	32	7	2	4	2
1	22	4	4	2	5
3	27	7	3	6	4
4	37	15	1	5	5
4	32	15	3	1	3
4	27	7	2	5	5
1	32	7	3	5	3
1	32	1.5	2	2	4
4	42	15	4	1	2
4	32	10	3	5	4
4	37	4	1	6	3
1	27	4	2	5	3
4	42	15	3	4	3
1	27	10	5	6	5
4	37	10	2	6	2
4	27	7	1	3	3
3	27	7	4	1	2
3	32	10	2	4	4
4	17.5	0.75	2	1	3
4	32	15	3	5	4
2	22	7	4	4	3
1	32	7	4	6	5
4	27	4	2	6	2
1	22	1.5	5	5	3
4	32	15	3	5	1
4	42	15	2	1	2
4	42	15	3	5	4
4	32	10	2	4	2
4	32	15	3	1	1
4	57	15	5	4	5
4	47	15	4	6	4
2	42	15	2	6	3
4	37	15	3	6	3
4	37	15	5	5	2
4	27	10	2	6	4
2	37	15	2	5	4
4	32	15	1	5	2
4	32	10	3	6	3
2	37	15	4	5	1
4	27	1.5	2	5	5
3	47	15	2	5	2
4	37	15	2	5	4
4	27	4	2	5	5
2	27	10	4	1	5
1	22	4	3	1	3
4	52	7	4	5	5
2	27	4	1	3	5
4	37	15	2	6	4
2	27	4	1	3	1
4	17.5	0.75	2	3	5
4	32	15	5	5	4
4	22	4	1	3	5
2	32	4	4	6	4
1	22	1.5	3	5	2
3	42	15	2	5	4
1	32	7	4	4	4
4	37	15	3	6	2
1	42	15	3	6	3
1	27	4	1	5	4
2	37	15	4	7	3
4	37	15	3	6	4
3	22	1.5	2	3	3
3	32	4	3	6	2
2	32	15	5	6	5
4	52	15	1	5	5
4	47	15	1	6	5
3	32	15	4	4	4
4	32	15	3	3	2
4	27	7	4	1	2
4	42	15	3	6	2
4	42	15	2	3	2
4	27	7	2	5	4
3	32	10	4	4	3
4	47	15	3	4	2
1	22	1.5	1	2	5
4	32	10	2	5	4
2	32	10	2	6	5
2	22	7	3	6	2
1	32	15	3	1	5
;

%let devdata=mydata;
%let valdata=mydata;
%let lower=0;
%let upper=4;
%let depv=Y;
%macro xvars;
z2 z3 z5 z7 z8
%mend xvars;
*Setting up for TOBIT - need  LOWER var set = missing;
data &devdata;  /* Development data set*/
set &devdata;
   lower=&lower;
   upper=&upper;
run;
data &valdata;  /* Validation data set */
set &valdata;
   lower=&lower;
   upper=&upper;
run;

PROC QLIM data=&devdata type=tobit maxiter=200 outest=parms  ;  /* coeffs written to PARMS */
predict: model &depv = %xvars / all covest=qml optmethod=qn ;
endogenous censored=(lb=lower ub=upper &depv);
ods output ParameterEstimates=tstats;
RUN;

ods output ParameterEstimates=tstats;
data tstats(keep= myvar coef T_STAT);
 set tstats end=last;
 MYVAR=parameter;
 COEF=estimate;
 T_STAT=coef/stderr;
run;

*=============================================================================================================;
%macro hardcode;
%let parms=parms;
%let lower=0;
%let upper=4;
%let depv=Y;
data coeff (keep=myvar coef);
length myvar $32;
set &parms end=last;
if last then call symput ('sigma', _SIGMA_);
/* Now we have SIGMA in  a macro var for later */
file './sel_var.txt';
put '%macro sel_var;';

array parametr{*} _numeric_;
length myvar $32;
do i = 1 to dim(parametr);
  if (parametr{i} ne .) then
  do;
     call vname (parametr{i}, myvar);
     coef=      (parametr{i} );
     if not(myvar in ("_LIKLHD_")) then
     do;
        if upcase(myvar)="INTERCEPT" then call symput('const', trim(left(coef)));
        if myvar ^="INTERCEPT" then
        do;
         put myvar;
         output;
        end;
     end;
  end;
end;
if last then put '%mend sel_var;';
* ---------------------------------------;

* Necessary to drop these two records;
data coeff;
set coeff end=last;
if myvar="&depv" then delete;
if myvar="_SIGMA_" then delete;
run;

*Now we count the number of records in COEFF;
data coeff;
set coeff end=last;
if last then call symput ('nvars',_n_);
/* Now we have number of parameters in a macro var */
run;

* Now we Write out hardcode;
data coeff ;
set coeff end=last;
sigma=put(&sigma,12.10);
upper=put(&upper,12.10);
lower=put(&lower,12.10);

file './hardcode_Qlim.txt';
if _n_=1 then do;
 put '%macro myhardcode;';
 put 'x_beta_hat = ';
 put  '    ' coef ' + ';
end;

if _n_>1 and _n_ <&nvars then put '     ' myvar ' * ' coef ' + ';
if last then do;
 put '     ' myvar ' * ' coef';';
 put 'lower='lower';';
 put 'upper='upper';';
 put 'sigma=' sigma ';';
 put 'phi_1=probnorm((lower-x_beta_hat)/sigma);';
 put 'phi_2=probnorm((upper-x_beta_hat)/sigma);';
 put 'pdf_1=exp(-0.5*((lower-x_beta_hat)/sigma)*((lower-x_beta_hat)/sigma))/sqrt(2*arcos(-1));';
 put 'pdf_2=exp(-0.5*((upper-x_beta_hat)/sigma)*((upper-x_beta_hat)/sigma))/sqrt(2*arcos(-1));';
 put 'pred_1=max(lower,min(upper,x_beta_hat) );'; 
 put 'pred_2=x_beta_hat+sigma*(pdf_1-pdf_2)/(phi_2-phi_1);';
*------------------------------------------------------------------------------------;
 put 'y_hat=phi_1*lower+x_beta_hat*(phi_2-phi_1)+sigma*(pdf_1-pdf_2)+(1-phi_2)*upper;';
 put '%mend myhardcode;';
end;
run;
%mend hardcode;
%hardcode;
data SCORED;
 set &valdata;
 %include './hardcode_Qlim.txt';
 %myhardcode;
run;

proc print data=scored (obs=25);
 var y x_beta_hat sigma phi_1 phi_2 pdf_1 pdf_2 pred_1 pred_2 y_hat;
run;