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[ab826e]: gmm_results.m Maximize Restore History

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gmm_results.m    102 lines (85 with data), 3.7 kB

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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 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, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
# 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., use 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
# obj_value: the value of the GMM objective function
#
# Please see gmm_example for information on how to use this
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);
n = rows(data);
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 = rows(weight) - rows(theta);
if df > 0
clabels = str2mat("Value","df","p-value");
a = [n*obj_value, df, 1 - chisquare_cdf(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*normal_cdf(abs(theta ./ se))];
clabels = str2mat("estimate", "st. err", "t-stat", "p-value");
printf("\n");
prettyprint(a, names, clabels);
printf("******************************************************\n");
endfunction