# 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

# GMM example file, shows initial consistent estimator,

# estimation of efficient weight, and second round

# efficient estimator

n = 1000;

k = 5;

x = [ones(n,1) rand(n,k-1)];

w = [x, rand(n,1)];

theta = [-k+1; ones(k-1,1)];

lambda = exp(x*theta);

y = randp(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");

title = "Poisson GMM trial";

control = {100,2,1,1};

# 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, title, scalecoef, control);

# 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, title, scalecoef, control, nslaves);