diff --git a/examples/12-mc-iterative-ols/clean b/examples/12-mc-iterative-ols/clean
new file mode 100755
index 0000000000000000000000000000000000000000..bdc7da19ab956a4a155e5fc8cbdf9a11ebbebff0
--- /dev/null
+++ b/examples/12-mc-iterative-ols/clean
@@ -0,0 +1,6 @@
+#!/bin/sh
+
+rm -rf example
+rm -rf +example
+rm -f example.log
+rm -f *.mat
\ No newline at end of file
diff --git a/examples/12-mc-iterative-ols/example.mod b/examples/12-mc-iterative-ols/example.mod
new file mode 100644
index 0000000000000000000000000000000000000000..32e0597706c3376a0cc35ed0b02bd969e4f03b59
--- /dev/null
+++ b/examples/12-mc-iterative-ols/example.mod
@@ -0,0 +1,104 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z y;
+
+varexo ex1 ex2 ex1bar ex2bar ez ey;
+
+parameters
+ rho_1 rho_2
+ 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 g;
+
+rho_1 = .9;
+rho_2 = -.2;
+
+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;
+
+g=.1;
+
+var_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar']);
+
+pac_model(var_model_name=toto, discount=beta, growth=g, model_name=pacman, undiff('eq:x1', 1), undiff('eq:x2', 1));
+
+model;
+
+[name='eq:y']
+y = rho_1*y(-1) + rho_2*y(-2) + ey;
+
+[name='eq:x1', data_type='nonstationary']
+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', data_type='nonstationary']
+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', data_type='nonstationary']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='zpac']
+diff(z) = e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) + pac_expectation(pacman) + 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;
+end;
+
+// Build the companion matrix of the VAR model (toto).
+get_companion_matrix('toto', '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));
+
+B = 20000;
+X = zeros(3,B);
+
+set_dynare_seed('default');
+options_.bnlms.set_dynare_seed_to_default = false;
+
+for i=1:B
+ e_c_m = .5;
+ c_z_1 = .2;
+ c_z_2 = -.1;
+ // Simulate the model for 500 periods
+ TrueData = simul_backward_model(initialconditions, 300);
+ // Define a structure describing the parameters to be estimated (with initial conditions).
+ eparams.e_c_m = rand();
+ eparams.c_z_1 = .5*randn();
+ eparams.c_z_2 = .5*randn();
+ //Â Define the dataset used for estimation
+ edata = TrueData;
+ edata.ez = dseries(NaN(TrueData.nobs, 1), 2000Q1, 'ez');
+ pac.estimate.iterative_ols('zpac', eparams, edata, 2005Q1:2000Q1+200);
+ X(1,i) = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
+ X(2,i) = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
+ X(3,i) = M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
+end
+
+mean(X, 2)
\ No newline at end of file
diff --git a/examples/12-mc-nls/clean b/examples/12-mc-nls/clean
new file mode 100755
index 0000000000000000000000000000000000000000..0d64aefdef6d2368cc070b20e575ca5e6ce127f4
--- /dev/null
+++ b/examples/12-mc-nls/clean
@@ -0,0 +1,7 @@
+#!/bin/sh
+
+rm -rf example
+rm -rf +example
+rm -f example.log
+rm -f *.mat
+rm *.m
diff --git a/examples/12-mc-nls/example.mod b/examples/12-mc-nls/example.mod
new file mode 100644
index 0000000000000000000000000000000000000000..7146a8e704063e726ddd25490737d7fed24f0dc7
--- /dev/null
+++ b/examples/12-mc-nls/example.mod
@@ -0,0 +1,100 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z y;
+
+varexo ex1 ex2 ex1bar ex2bar ez ey;
+
+parameters
+ rho_1 rho_2
+ 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 ;
+
+rho_1 = .9;
+rho_2 = -.2;
+
+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;
+
+var_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar']);
+
+pac_model(var_model_name=toto, discount=beta, model_name=pacman, undiff('eq:x1', 1), undiff('eq:x2', 1));
+
+model;
+
+[name='eq:y']
+y = rho_1*y(-1) + rho_2*y(-2) + ey;
+
+[name='eq:x1', data_type='nonstationary']
+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', data_type='nonstationary']
+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', data_type='nonstationary']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar', data_type='nonstationary']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='zpac']
+diff(z) = e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) + pac_expectation(pacman) + 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;
+end;
+
+// Build the companion matrix of the VAR model (toto).
+get_companion_matrix('toto', '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));
+
+
+B = 20000;
+X = zeros(3,B);
+set_dynare_seed('default');
+
+options_.bnlms.set_dynare_seed_to_default = false;
+
+for i=1:B
+ // Simulate the model for 500 periods
+ TrueData = simul_backward_model(initialconditions, 300);
+ // Define a structure describing the parameters to be estimated (with initial conditions).
+ eparams.e_c_m = rand();
+ eparams.c_z_1 = .5*randn();
+ eparams.c_z_2 = .5*randn();
+ //Â Define the dataset used for estimation
+ edata = TrueData;
+ edata.ez = dseries(NaN(TrueData.nobs, 1), 200Q1, 'ez');
+ pac.estimate.nls('zpac', eparams, edata, 2005Q1:2000Q1+200);
+ X(1,i) = M_.params(strmatch('e_c_m', M_.param_names, 'exact'));
+ X(2,i) = M_.params(strmatch('c_z_1', M_.param_names, 'exact'));
+ X(3,i) = M_.params(strmatch('c_z_2', M_.param_names, 'exact'));
+end
+
+mean(X, 2)
\ No newline at end of file