Logo Search packages:      
Sourcecode: octave-econometrics version File versions

gmm_results.m

# Copyright (C) 2003,2004,2005  Michael Creel <michael.creel@uab.es>
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; If not, see <http://www.gnu.org/licenses/>.

# usage: [theta, V, obj_value] =
#  gmm_results(theta, data, weight, moments, momentargs, names, title, unscale, control, nslaves)
#
# inputs:
#      theta: column vector initial parameters
#       data: data matrix
#     weight: the GMM weight matrix
#    moments: name of function computes the moments
#             (should return nXg matrix of contributions)
# momentargs: (cell) additional inputs needed to compute moments.
#             May be empty ("")
#      names: vector of parameter names
#             e.g., names = str2mat("param1", "param2");
#      title: string, describes model estimated
#    unscale: (optional) cell that holds means and std. dev. of data
#             (see scale_data)
#    control: (optional) BFGS or SA controls (see bfgsmin and samin). May be empty ("").
#    nslaves: (optional) number of slaves if executed in parallel
#             (requires MPITB)
#
# outputs:
# theta: GMM estimated parameters
# V: estimate of covariance of parameters. Assumes the weight matrix
#    is optimal (inverse of covariance of moments)
# obj_value: the value of the GMM objective function
#
# please type "gmm_example" while in octave to see an example


function [theta, V, obj_value] = gmm_results(theta, data, weight, moments, momentargs, names, title, unscale, control, nslaves)

  if nargin < 10 nslaves = 0; endif # serial by default

      if nargin < 9
            [theta, obj_value, convergence] = gmm_estimate(theta, data, weight, moments, momentargs, "", nslaves);
      else
            [theta, obj_value, convergence] = gmm_estimate(theta, data, weight, moments, momentargs, control, nslaves);
      endif


      m = feval(moments, theta, data, momentargs); # find out how many obsns. we have
      n = rows(m);

      if convergence == 1
            convergence="Normal convergence";
      else
            convergence="No convergence";
      endif

      V = gmm_variance(theta, data, weight, moments, momentargs);

      # unscale results if argument has been passed
      # this puts coefficients into scale corresponding to the original data
      if nargin > 7
            if iscell(unscale)
                  [theta, V] = unscale_parameters(theta, V, unscale);
            endif
      endif

      [theta, V] = delta_method("parameterize", theta, {data, moments, momentargs}, V);

      k = rows(theta);
      se = sqrt(diag(V));

      printf("\n\n******************************************************\n");
      disp(title);
      printf("\nGMM Estimation Results\n");
      printf("BFGS convergence: %s\n", convergence);
      printf("\nObjective function value: %f\n", obj_value);
      printf("Observations: %d\n", n);

      junk = "X^2 test";
      df = n - k;
      if df > 0
            clabels = str2mat("Value","df","p-value");
            a = ;
            printf("\n");
            prettyprint(a, junk, clabels);
      else
            disp("\nExactly identified, no spec. test");
      end;

      # results for parameters
      a =;
      clabels = str2mat("estimate", "st. err", "t-stat", "p-value");
      printf("\n");
      prettyprint(a, names, clabels);

      printf("******************************************************\n");
endfunction

Generated by  Doxygen 1.6.0   Back to index