Maximum Likelihood Estimation with Analytic Gradients#

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

Key example features#

  • Usages of data from the file maxlikmttobit.dat (included with maxlikmt).

  • User defined likelihood function, lpr with four inputs:

    • A parameter vector.

    • Additional X and y data matrices, which are passed to maxlikmt() 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 maxlikmt;

// 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/maxlikmt/examples/maxlikmttobit.dat"));

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

// Declare control structure
struct maxlikmtControl c0;
c0 = maxlikmtcontrolcreate;

// Print Iterations to screen
c0.printiters = 1;

// Change descent algorithm to use BHHH
c0.algorithm = 4;

// Set tolerance level
c0.tol = 1e-6;

// Place bounds on coefficients
// -10 < b0 < 10
//- 10 < b1, b2, b3 < 10
// 0.1 < s2 < 10
c0.Bounds = { -10 10,
              -10 10,
              -10 10,
              -10 10,
              .1 10 };

// Declare 'out' to be a maxlikmtResults
// struct to hold optimization results
struct maxlikmtResults out;
out = maxlikmtprt(maxlikmt(&lpr, p0, x, y, c0));

Results#

The maxlikmtprt() procedure prints three output tables:

  • Estimation results.

  • Correlation matrix of parameters.

  • Wald confidence limits.

Estimation results#

===============================================================================
 MAXLIKMT Version 3.0.0
===============================================================================

return code =    0
normal convergence

Log-likelihood        -44.8988
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]           1.4303        0.0338     42.348   0.0000      0.0000
x[2,1]           0.4948        0.0355     13.953   0.0000      0.0000
x[3,1]           0.4955        0.0413     12.011   0.0000      0.0000
x[4,1]           0.4119        0.0355     11.596   0.0000      0.0000
x[5,1]           0.1000        0.0132      7.587   0.0000     90.9995

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 maxlikmt() 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      0.069139065      -0.24058113      0.056496522     -0.088492586
 0.069139065                1      -0.30744504     -0.060911279       0.04713576
 -0.24058113      -0.30744504                1      -0.31863882      0.054598226
 0.056496522     -0.060911279      -0.31863882                1      0.036705333
-0.088492586       0.04713576      0.054598226      0.036705333                1

Confidence intervals#

Wald Confidence Limits

                              0.95 confidence limits
Parameters    Estimates     Lower Limit   Upper Limit      Gradient
----------------------------------------------------------------------
x[1,1]           1.4303        1.3632          1.4973        0.0000
x[2,1]           0.4948        0.4244          0.5652        0.0000
x[3,1]           0.4955        0.4136          0.5774        0.0000
x[4,1]           0.4119        0.3414          0.4824        0.0000
x[5,1]           0.1000        0.0738          0.1262       90.9995

Number of iterations    16
Minutes to convergence     0.00442
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