Mercurial > forge
changeset 2324:b94cc195d5b6 octave-forge
Example for nonlinear least squares estimation
author | mcreel |
---|---|
date | Tue, 13 Jun 2006 13:25:13 +0000 |
parents | 3a720be3b267 |
children | 61855097ec0c |
files | main/econometrics/nls_example.m |
diffstat | 1 files changed, 60 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/main/econometrics/nls_example.m Tue Jun 13 13:25:13 2006 +0000 @@ -0,0 +1,60 @@ +## Copyright (C) 2006 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 + +## Example to show how to use NLS + +# Generate data +n = 100; # how many observations? + +# the explanatory variables: note that they have unequal scales +x = [ones(n,1) rand(n,2)]; +theta = 1:3; # true coefficients are 1,2,3 +theta = theta'; + +lambda = exp(x*theta); +y = randp(lambda); # generate the dependent variable + +# example objective function for nls +function [obj_contrib, score] = nls_example_obj(theta, data, otherargs) + y = data(:,1); + x = data(:,2:columns(data)); + lambda = exp(x*theta); + errors = y - lambda; + obj_contrib = errors .* errors; + score = "na"; +endfunction + + +#################################### +# define arguments for nls_estimate # +#################################### + +# starting values +theta = zeros(3,1); + +# data +data = [y, x]; + +# name of model to estimate +model = "nls_example_obj"; +modelargs = {}; # none required for this obj fn. + + +# controls for bfgsmin +control = {50,1,1,1}; + +printf("\n\NLS estimation example\n"); +[theta, obj_value, convergence] = nls_estimate(theta, data, model, modelargs, control);