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gmm_example.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/>.

# GMM example file, shows initial consistent estimator,
# estimation of efficient weight, and second round
# efficient estimator


n = 1000;
k = 5;

x = [ones(n,1) randn(n,k-1)];
w = [x, rand(n,1)];
theta_true = ones(k,1);
lambda = exp(x*theta_true);
y = poisson_rnd(lambda);
[xs, scalecoef] = scale_data(x);

# The arguments for gmm_estimate
theta = zeros(k,1);
data = [y xs w];
weight = eye(columns(w));
moments = "poisson_moments";
momentargs = {k}; # needed to know where x ends and w starts

# additional args for gmm_results
names = str2mat("theta1", "theta2", "theta3", "theta4", "theta5");
gmmtitle = "Poisson GMM trial";
control = {100,0,1,1};


# initial consistent estimate: only used to get efficient weight matrix, no screen output
[theta, obj_value, convergence] = gmm_estimate(theta, data, weight, moments, momentargs, control);

# efficient weight matrix
# this method is valid when moments are not autocorrelated
# the user is reponsible to properly estimate the efficient weight
m = feval(moments, theta, data, momentargs);
weight = inverse(cov(m));

# second round efficient estimator
gmm_results(theta, data, weight, moments, momentargs, names, gmmtitle, scalecoef, control);
printf("\nThe true parameter values used to generate the data:\n");
prettyprint(theta_true, names, "value");

# Example doing estimation in parallel on a cluster (requires MPITB)
# uncomment the following if you have MPITB installed
# nslaves = 1;
# theta = zeros(k,1);
# nslaves = 1;
# title = "GMM estimation done in parallel";
# gmm_results(theta, data, weight, moments, momentargs, names, gmmtitle, scalecoef, control, nslaves);

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