Ordered Probit Model in Ox

Question

I'm learning Ox because it's flexible when it comes to complex econometrics models related to my work. But I'm struggling with my ordered probit model. For a simple probit model (order = 2), the function is as follows:

decl g_mX, g_mY;


fProbit(const vP, const adFunc, const avScore,
const amHessian)
{
    decl prob = probn(g_mX * vP); // vP is column vector
    
    adFunc[0] = double(
       meanc(g_mY .* log(prob) + (1-g_mY) .* log(1-prob)));

return 1; // 1 indicates success
}

In the 'main' section, I simulate my data and call the BFGS maximizer integrated in Ox package "maximize" :

main()

{
    decl ct, mx, vbeta, veps;
    
    decl vp, dfunc, ir;
    
    println("Probit example 1, run on ", date());

    ct = 546 ; 
    mx = ranbinomial(ct, 1, 1, 0.35)~ ranbinomial(ct, 2, 1, 0.45)~ranbinomial(ct,8, 1, 0.48) ;
    veps = rann(ct, 1);
    g_mX = 1~mx[][1:10]; // regressors: 1, Marital, Gender, Age, etc.
    vp0 = <.7034119;-.0416732;-.2973156;-.1419538 ;-.4537812; -.3036446;-.1783031 ;  .0619869; .0310086; .0011324; -.0310052 > ; // starting values
    g_mY = (g_mX*vp0 + veps) .> 0 ; // dependent variable: 0,1 dummy
 
    vp = <0 ;0 ;0;0 ;0;0;0 ;0;0;0; 0 >;

    MaxControl(-1, 1); // print each iteration maximize

    ir = MaxBFGS(fProbit, &vp, &dfunc, 0, TRUE);

    print("\n", MaxConvergenceMsg(ir),
        " using numerical derivatives",
        "\n Function value = ", dfunc * rows(g_mY),
        "; parameters:", vp);
    
}

It works well, with a strong convergence and vp ~ vp0.

Now suppose I have an order greater than two (dependent variable with 3 or more categories). How you code this ordered probit?

Many thanks

Ordered Probit Model in Ox

0 Answers0