Added interface to lsqnonlin (Mathworks' optimization toolbox) in pac.estimate.nls.
Stéphane Adjemia (Scylla)
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c79be57447
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d501d6d511
GPG Key ID:
A6D44CB9C64CE77B
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@ -62,9 +62,17 @@ function nls(eqname, params, data, range, optimizer, varargin)
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global M_ oo_ options_
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is_gauss_newton = false;
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is_lsqnonlin = false;
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objective = 'ssr_';
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if nargin>4 && isequal(optimizer, 'GaussNewton')
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is_gauss_newton = true;
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if nargin>4 && (isequal(optimizer, 'GaussNewton') || isequal(optimizer, 'lsqnonlin'))
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switch optimizer
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case 'GaussNewton'
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is_gauss_newton = true;
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case 'lsqnonlin'
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is_lsqnonlin = true;
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otherwise
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% Cannot happen.
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end
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objective = 'r_';
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end
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@ -184,6 +192,9 @@ else
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switch optimizer
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case 'GaussNewton'
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% Nothing to do here.
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case 'lsqnonlin'
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bounds = ones(length(params0),1)*[-10,10];
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bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.pacman.ec.params)),1) = .0;
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case 'fmincon'
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if isoctave
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error('Optimization algorithm ''fmincon'' is not available under Octave')
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@ -235,6 +246,7 @@ else
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msg = sprintf('%s - %s\n', msg, 'fminsearch');
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msg = sprintf('%s - %s\n', msg, 'simplex');
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msg = sprintf('%s - %s\n', msg, 'annealing');
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msg = sprintf('%s - %s\n', msg, 'lsqnonlin');
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msg = sprintf('%s - %s\n', msg, 'GaussNewton');
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error(msg)
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end
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@ -273,6 +285,13 @@ end
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if is_gauss_newton
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[params1, SSR, exitflag] = gauss_newton(resfun, params0);
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elseif is_lsqnonlin
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if ismember('levenberg-marquardt', varargin)
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% Levenberg Marquardt does not handle boundary constraints.
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[params1, SSR, ~, exitflag] = lsqnonlin(resfun, params0, [], [], optimset(varargin{:}));
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else
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[params1, SSR, ~, exitflag] = lsqnonlin(resfun, params0, bounds(:,1), bounds(:,2), optimset(varargin{:}));
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end
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else
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% Estimate the parameters by minimizing the sum of squared residuals.
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[params1, SSR, exitflag] = dynare_minimize_objective(ssrfun, params0, ...
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@ -409,6 +409,8 @@ MODFILES = \
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pac/trend-component-14/substitution.mod \
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pac/trend-component-15/example.mod \
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pac/trend-component-16/example.mod \
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pac/trend-component-17/example.mod \
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pac/trend-component-18/example.mod \
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estimation/univariate/bayesian.mod \
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dynare-command-options/ramst.mod
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@ -0,0 +1,8 @@
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#!/bin/sh
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rm -rf example
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rm -rf +example
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rm -f example.log
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rm -f *.mat
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rm -f *.m
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rm -f *.dat
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@ -0,0 +1,159 @@
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// --+ options: json=compute, stochastic +--
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var x1 x2 x1bar x2bar z y x;
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varexo ex1 ex2 ex1bar ex2bar ez ey ex;
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parameters
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rho_1 rho_2 rho_3 rho_4
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a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
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a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
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e_c_m c_z_1 c_z_2 beta
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lambda;
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rho_1 = .9;
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rho_2 = -.2;
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rho_3 = .4;
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rho_4 = -.3;
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a_x1_0 = -.9;
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a_x1_1 = .4;
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a_x1_2 = .3;
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a_x1_x2_1 = .1;
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a_x1_x2_2 = .2;
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a_x2_0 = -.9;
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a_x2_1 = .2;
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a_x2_2 = -.1;
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a_x2_x1_1 = -.1;
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a_x2_x1_2 = .2;
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beta = .2;
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e_c_m = .5;
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c_z_1 = .2;
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c_z_2 = -.1;
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lambda = 0.5; // Share of optimizing agents.
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trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], targets=['eq:x1bar', 'eq:x2bar']);
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pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
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model;
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[name='eq:y']
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y = rho_1*y(-1) + rho_2*y(-2) + ey;
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[name='eq:x']
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x = rho_3*x(-1) + rho_4*x(-2) + ex;
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[name='eq:x1']
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diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;
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[name='eq:x2']
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diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;
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[name='eq:x1bar']
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x1bar = x1bar(-1) + ex1bar;
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[name='eq:x2bar']
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x2bar = x2bar(-1) + ex2bar;
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[name='zpac']
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diff(z) = lambda*(e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) + pac_expectation(pacman)) + (1-lambda)*( y + x) + ez;
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end;
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shocks;
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var ex1 = 1.0;
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var ex2 = 1.0;
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var ex1bar = 1.0;
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var ex2bar = 1.0;
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var ez = 1.0;
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var ey = 0.1;
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var ex = 0.1;
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end;
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// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
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pac.initialize('pacman');
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// Update the parameters of the PAC expectation model (h0 and h1 vectors).
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pac.update.expectation('pacman');
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// Set initial conditions to zero. Please use more sensible values if any...
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initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
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// Simulate the model for 500 periods
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TrueData = simul_backward_model(initialconditions, 300);
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// Define a structure describing the parameters to be estimated (with initial conditions).
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clear eparams
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eparams.e_c_m = .9;
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eparams.c_z_1 = .5;
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eparams.c_z_2 = .2;
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eparams.lambda = .7;
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// Define the dataset used for estimation
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edata = TrueData;
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edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
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tic
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pac.estimate.nls('zpac', eparams, edata, 2005Q1:2005Q1+200, 'csminwel', 'verbosity', 0);
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toc
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skipline(1)
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e_c_m_nls = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
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c_z_1_nls = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
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c_z_2_nls = M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
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lambda_nls = M_.params(strmatch('lambda', M_.param_names, 'exact'));
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disp(sprintf('Estimate of e_c_m: %f', e_c_m_nls))
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disp(sprintf('Estimate of c_z_1: %f', c_z_1_nls))
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disp(sprintf('Estimate of c_z_2: %f', c_z_2_nls))
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disp(sprintf('Estimate of lambda: %f', lambda_nls))
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skipline(2)
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// Define a structure describing the parameters to be estimated (with initial conditions).
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clear eparams
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eparams.e_c_m = .9;
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eparams.c_z_1 = .5;
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eparams.c_z_2 = .2;
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eparams.lambda = .0;
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// Define the dataset used for estimation
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edata = TrueData;
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edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
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tic
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pac.estimate.nls('zpac', eparams, edata, 2005Q1:2005Q1+200, 'lsqnonlin', 'Algorithm', 'levenberg-marquardt');
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toc
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skipline(1)
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e_c_m_lsqnonlin = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
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c_z_1_lsqnonlin = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
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c_z_2_lsqnonlin= M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
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lambda_lsqnonlin = M_.params(strmatch('lambda', M_.param_names, 'exact'));
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disp(sprintf('Estimate of e_c_m: %f', e_c_m_lsqnonlin))
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disp(sprintf('Estimate of c_z_1: %f', c_z_1_lsqnonlin))
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disp(sprintf('Estimate of c_z_2: %f', c_z_2_lsqnonlin))
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disp(sprintf('Estimate of lambda: %f', lambda_lsqnonlin))
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if abs(e_c_m_nls-e_c_m_lsqnonlin)>.01
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error('Gauss Newton and direct SSR minimization do not provide consistent estimates (e_c_m)')
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end
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if abs(c_z_1_nls-c_z_1_lsqnonlin)>.01
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error('Gauss Newton and direct SSR minimization do not provide consistent estimates (c_z_1)')
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end
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if abs(c_z_2_nls-c_z_2_lsqnonlin)>.01
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error('Gauss Newton and direct SSR minimization do not provide consistent estimates (c_z_2)')
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end
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if abs(lambda_nls-lambda_lsqnonlin)>.01
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error('Gauss Newton and direct SSR minimization do not provide consistent estimates (lambda)')
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end
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