"MNLEST.GPR: program to compute maximum likelihood estimates of
            binomial logit model as a hint for Econ 551 midterm exam

            By John Rust, Yale University, March, 1997. ";

 /* STEP 1: Load in ASCII datasets and convert to GAUSS data sets
            with alternative-specific covariates for use in general
            form of multinomial logit model coded in EVAL.G.
	    Note the 3 coefficients (const, slope_a and slope_b) are
	    normalized to 0 for alternative 1. So estimate 3
	    coefficients for alternative 2.*/

   load dat[]=data2.asc;
   dat=reshape(dat,2000,3);
/* incorrect code:  y=(dat[.,1] .== 2); y=1 if choose alt 2, y =0 otherwise */
/* corrected code:
   y=(dat[.,1] .== 1);       /* y=1 if choose alt 1, y =0 otherwise */
   x=ones(2000,1)~dat[.,2:3];
   dat=y~x;
   let vnames=const slope_a slope_b;
   dat=saved(dat,"data3","vnames");

   /* Step 2: set up input variables to estimate MNL model in
              DATA2.ASC, performing a derivative check first
              to make sure likelihood and derivatives are correctly
              coded in EVAL.G */

   iloc=seqa(2,1,3);
   cloc=0;
   dloc=1;
   q=zeros(3,1);
   infile="data3";
   outfile="data3.est";
   qsav="q_data3";
   dvar="y";
   let ivar=const slope_a slope_b;


/* Uncomment this line if you would like to run a derivative check,
   i.e. a comparison of numerical and analytical derivatives of the
   log-likelihood function 

   call drvchk(dloc,iloc,cloc,q,infile,2000,outfile,qsav,&eval);  */


   /* Below is the main procedure that maximizes the likelihood function */

   call max(dvar,dloc,ivar,iloc,cloc,q,infile,500,outfile,qsav,&eval);