diff --git a/matlab/+pac/+estimate/nls.m b/matlab/+pac/+estimate/nls.m
index 44fd9416d..779f10ab3 100644
--- a/matlab/+pac/+estimate/nls.m
+++ b/matlab/+pac/+estimate/nls.m
@@ -62,9 +62,17 @@ function nls(eqname, params, data, range, optimizer, varargin)
 global M_ oo_ options_
 
 is_gauss_newton = false;
+is_lsqnonlin = false;
 objective = 'ssr_';
-if nargin>4 && isequal(optimizer, 'GaussNewton')
-    is_gauss_newton = true;
+if nargin>4 && (isequal(optimizer, 'GaussNewton') || isequal(optimizer, 'lsqnonlin'))
+    switch optimizer
+      case 'GaussNewton'
+        is_gauss_newton = true;
+      case 'lsqnonlin'
+        is_lsqnonlin = true;
+      otherwise
+        % Cannot happen.
+    end
     objective = 'r_';
 end
 
@@ -184,6 +192,9 @@ else
     switch optimizer
       case 'GaussNewton'
         % Nothing to do here.
+      case 'lsqnonlin'
+        bounds = ones(length(params0),1)*[-10,10];
+        bounds(strcmp(fieldnames(params), M_.param_names(M_.pac.pacman.ec.params)),1)  = .0;
       case 'fmincon'
         if isoctave
             error('Optimization algorithm ''fmincon'' is not available under Octave')
@@ -235,6 +246,7 @@ else
         msg = sprintf('%s - %s\n', msg, 'fminsearch');
         msg = sprintf('%s - %s\n', msg, 'simplex');
         msg = sprintf('%s - %s\n', msg, 'annealing');
+        msg = sprintf('%s - %s\n', msg, 'lsqnonlin');
         msg = sprintf('%s - %s\n', msg, 'GaussNewton');
         error(msg)
     end
@@ -273,6 +285,13 @@ end
 
 if is_gauss_newton
     [params1, SSR, exitflag] = gauss_newton(resfun, params0);
+elseif is_lsqnonlin
+    if ismember('levenberg-marquardt', varargin)
+        % Levenberg Marquardt does not handle boundary constraints.
+        [params1, SSR, ~, exitflag] = lsqnonlin(resfun, params0, [], [], optimset(varargin{:}));
+    else
+        [params1, SSR, ~, exitflag] = lsqnonlin(resfun, params0, bounds(:,1), bounds(:,2), optimset(varargin{:}));
+    end
 else
     % Estimate the parameters by minimizing the sum of squared residuals.
     [params1, SSR, exitflag] = dynare_minimize_objective(ssrfun, params0, ...
diff --git a/tests/Makefile.am b/tests/Makefile.am
index 272ab21e6..d42d90bd4 100644
--- a/tests/Makefile.am
+++ b/tests/Makefile.am
@@ -409,6 +409,8 @@ MODFILES = \
     pac/trend-component-14/substitution.mod \
     pac/trend-component-15/example.mod \
     pac/trend-component-16/example.mod \
+    pac/trend-component-17/example.mod \
+    pac/trend-component-18/example.mod \
     estimation/univariate/bayesian.mod \
     dynare-command-options/ramst.mod
 
diff --git a/tests/pac/trend-component-18/clean b/tests/pac/trend-component-18/clean
new file mode 100755
index 000000000..cf3492a28
--- /dev/null
+++ b/tests/pac/trend-component-18/clean
@@ -0,0 +1,8 @@
+#!/bin/sh
+
+rm -rf example
+rm  -rf +example
+rm -f example.log
+rm -f *.mat
+rm -f *.m
+rm -f *.dat
diff --git a/tests/pac/trend-component-18/example.mod b/tests/pac/trend-component-18/example.mod
new file mode 100644
index 000000000..26418c850
--- /dev/null
+++ b/tests/pac/trend-component-18/example.mod
@@ -0,0 +1,159 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z y x;
+
+varexo ex1 ex2 ex1bar ex2bar ez ey ex;
+
+parameters
+       rho_1 rho_2 rho_3 rho_4
+       a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 beta
+       lambda;
+
+rho_1 =  .9;
+rho_2 = -.2;
+rho_3 =  .4;
+rho_4 = -.3;
+
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .2;
+e_c_m =  .5;
+c_z_1 =  .2;
+c_z_2 = -.1;
+
+lambda = 0.5; // Share of optimizing agents.
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], targets=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+model;
+
+[name='eq:y']
+y = rho_1*y(-1) + rho_2*y(-2) + ey;
+
+[name='eq:x']
+x = rho_3*x(-1) + rho_4*x(-2) + ex;
+
+[name='eq:x1']
+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;     
+
+[name='eq:x2']
+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;     
+
+[name='eq:x1bar']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='zpac']
+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;
+
+end;
+
+shocks;
+    var ex1 = 1.0;
+    var ex2 = 1.0;
+    var ex1bar = 1.0;
+    var ex2bar = 1.0;
+    var ez = 1.0;
+    var ey = 0.1;
+    var ex = 0.1;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+// Set initial conditions to zero. Please use more sensible values if any...
+initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+
+// Simulate the model for 500 periods
+TrueData = simul_backward_model(initialconditions, 300);
+
+// Define a structure describing the parameters to be estimated (with initial conditions). 
+clear eparams
+eparams.e_c_m  =  .9;
+eparams.c_z_1  =  .5;
+eparams.c_z_2  =  .2;
+eparams.lambda =  .7;
+
+// Define the dataset used for estimation
+edata = TrueData;
+edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
+
+tic
+pac.estimate.nls('zpac', eparams, edata, 2005Q1:2005Q1+200, 'csminwel', 'verbosity', 0);
+toc
+skipline(1)
+
+e_c_m_nls = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
+c_z_1_nls = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
+c_z_2_nls = M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
+lambda_nls = M_.params(strmatch('lambda', M_.param_names, 'exact'));
+
+disp(sprintf('Estimate of e_c_m: %f', e_c_m_nls))
+disp(sprintf('Estimate of c_z_1: %f', c_z_1_nls))
+disp(sprintf('Estimate of c_z_2: %f', c_z_2_nls))
+disp(sprintf('Estimate of lambda: %f', lambda_nls))
+
+skipline(2)
+
+// Define a structure describing the parameters to be estimated (with initial conditions). 
+clear eparams
+eparams.e_c_m  =  .9;
+eparams.c_z_1  =  .5;
+eparams.c_z_2  =  .2;
+eparams.lambda =  .0;
+
+// Define the dataset used for estimation
+edata = TrueData;
+edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
+
+tic
+pac.estimate.nls('zpac', eparams, edata, 2005Q1:2005Q1+200, 'lsqnonlin', 'Algorithm', 'levenberg-marquardt');
+toc
+
+skipline(1)
+
+e_c_m_lsqnonlin = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
+c_z_1_lsqnonlin = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
+c_z_2_lsqnonlin= M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
+lambda_lsqnonlin = M_.params(strmatch('lambda', M_.param_names, 'exact'));
+
+disp(sprintf('Estimate of e_c_m: %f', e_c_m_lsqnonlin))
+disp(sprintf('Estimate of c_z_1: %f', c_z_1_lsqnonlin))
+disp(sprintf('Estimate of c_z_2: %f', c_z_2_lsqnonlin))
+disp(sprintf('Estimate of lambda: %f', lambda_lsqnonlin))
+
+if abs(e_c_m_nls-e_c_m_lsqnonlin)>.01
+   error('Gauss Newton and direct SSR minimization do not provide consistent estimates (e_c_m)')
+end
+
+if abs(c_z_1_nls-c_z_1_lsqnonlin)>.01
+   error('Gauss Newton and direct SSR minimization do not provide consistent estimates (c_z_1)')
+end
+
+if abs(c_z_2_nls-c_z_2_lsqnonlin)>.01
+   error('Gauss Newton and direct SSR minimization do not provide consistent estimates (c_z_2)')
+end
+
+if abs(lambda_nls-lambda_lsqnonlin)>.01
+   error('Gauss Newton and direct SSR minimization do not provide consistent estimates (lambda)')
+end
\ No newline at end of file