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## 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 3 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 = char("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 = char("Value","df","p-value");
a = [n*obj_value, df, 1 - chi2cdf(n*obj_value, df)];
printf("\n");
prettyprint(a, junk, clabels);
else
disp("\nExactly identified, no spec. test");
end;
# results for parameters
a =[theta, se, theta./se, 2 - 2*normcdf(abs(theta ./ se))];
clabels = char("estimate", "st. err", "t-stat", "p-value");
printf("\n");
prettyprint(a, names, clabels);
printf("******************************************************\n");
endfunction
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