## [63736b]: gmm_example.m  Maximize  Restore  History

### 72 lines (57 with data), 2.3 kB

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70``` ```# Copyright (C) 2003,2004 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 # This also shows how do use data scaling - WHICH YOU SHOULD DO! 1; # the form a user-written moment function should take function m = mymoments(theta, data, momentargs) k = momentargs{1}; # use this so that data can hold dep, indeps, and instr y = data(:,1); x = data(:,2:k+1); w = data(:, k+2:columns(data)); lambda = exp(x*theta); e = y ./ lambda - 1; m = dmult(e, w); endfunction n = 1000; k = 5; x = [ones(n,1) rand(n,k-1)]; w = [x, rand(n,1)]; theta = ones(k,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 = "mymoments"; 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 = {100,0,1,1}; # initial consistent estimate: only used to get efficient weight matrix, no screen output [theta, obj_value, convergence] = gmm_estimate(theta, data, weight, moments, momentargs); # 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); ```