Maximum Likelihood Estimation with Analytic Gradients¶

This GAUSS maximum likelihood example demonstrates the use of CMLMT to estimate parameters of a tobit modelwith analytic first derivatives.

Key example features¶

Usages of data from the file cmlmttobit.dat (included with cmlmt).
User defined likelihood function, lpr with four inputs:
- A parameter vector.
- Additional X and y data matrices, which are passed to cmlmt`() as optional arguments.
- The required ind input.
The inclusion of analytic gradient computations, as specified in the lpr function.

Code for estimation¶

/*
**   Maximum likelihood tobit model
*/
new;
library cmlmt;

// Tobit likelihood function with 4 inputs
//    i.      p      - The parameter vector
//    ii-iii. x and y - Extra data needed by the objective procedure
//    ii.     ind     - The indicator vector
proc lpr(p, x, y, ind);
    local s2, b0, b, yh, u, res, g1, g2;

    // Declare 'mm' to be a modelResults
    // struct local to this procedure
    struct modelResults mm;

    // Parameters
    b0 = p[1];
    b = p[2:4];
    s2 = p[5];

    // Function computations
    yh = b0 + x * b;
    res = y - yh;
    u = y[., 1] ./= 0;

    // If first element of 'ind' is non-zero,
    // compute function evaluation
    if ind[1];
        mm.function = u.*lnpdfmvn(res, s2) + (1 - u).*(ln(cdfnc(yh/sqrt(s2))));
    endif;

    // If second element of 'ind' is non-zero,
    // compute function evaluation
    if ind[2];
      yh = yh/sqrt(s2);
      g1 = ((res~x.*res)/s2) ~ ((res.*res/s2) - 1)/(2*s2);
      g2 = ( -( ones(rows(x), 1) ~ x )/sqrt(s2) ) ~ (yh/(2*s2));
      g2 = (pdfn(yh)./cdfnc(yh)).*g2;
      mm.gradient = u.*g1 + (1 - u).*g2;
    endif;

    // Return modelResults struct
    retp(mm);

endp;

// Set parameter starting values
p0 = {1, 1, 1, 1, 1};

// Load data
z = loadd(getGAUSSHome("pkgs/cmlmt/examples/cmlmttobit.dat"));

// Separate X and y
y = z[., 1];
x = z[., 2:4];

// Declare 'out' to be a cmlmtResults
// struct to hold optimization results
struct cmlmtResults out;
out = cmlmtprt(cmlmt(&lpr, p0, x, y));

Results¶

The cmlmtprt() procedure prints three output tables:

Estimation results.
Correlation matrix of parameters.
Wald confidence limits.

Estimation results¶

===============================================================================
 CMLMT Version 3.0.0
===============================================================================

return code =    0
normal convergence

Log-likelihood        -43.9860
Number of cases     100

Covariance of the parameters computed by the following method:
ML covariance matrix
  Parameters    Estimates     Std. err.  Est./s.e.  Prob.    Gradient
---------------------------------------------------------------------
x[1,1]          1.4253        0.0376      37.925   0.0000      0.0000
x[2,1]          0.4976        0.0394      12.642   0.0000      0.0000
x[3,1]          0.4992        0.0458      10.889   0.0000      0.0000
x[4,1]          0.4141        0.0394      10.506   0.0000      0.0000
x[5,1]          0.1231        0.0196       6.284   0.0000      0.0000

The estimation results reports:

That the model has converged normally with a return code of 0. Any return code other than 0, indicates an issue with convergence. The cmlmt() documentation provides details on how to interpret non-zero return codes.
The log-likelihood value and number of cases.
Parameter estimates, standard errors, t-statistics and associated p-values, and gradients.

Parameter correlations¶

Correlation matrix of the parameters
           1      0.067006788      -0.24418626       0.05530654      -0.10868104
 0.067006788                1      -0.30495236     -0.061965451       0.05808199
 -0.24418626      -0.30495236                1       -0.3165649      0.067030893
  0.05530654     -0.061965451       -0.3165649                1       0.04466025
 -0.10868104       0.05808199      0.067030893       0.04466025                1

Confidence intervals¶

Wald Confidence Limits

                              0.95 confidence limits
  Parameters    Estimates     Lower Limit   Upper Limit   Gradient
----------------------------------------------------------------------
x[1,1]    1.4253        1.3507        1.4999        0.0000
x[2,1]    0.4976        0.4195        0.5757        0.0000
x[3,1]    0.4992        0.4082        0.5903        0.0000
x[4,1]    0.4141        0.3358        0.4923        0.0000
x[5,1]    0.1231        0.0842        0.1620        0.0000

Basic Maximum Likelihood Estimation

Maximum Likelihood Estimation with Nonlinear Equality Constraints