diff --git a/matlab/+mom/default_option_mom_values.m b/matlab/+mom/default_option_mom_values.m
index 5ce9d34a94e491d76a27b9fb754a4d2c6dccf850..6587dea748a3aa72740e82d89968d04cc567799a 100644
--- a/matlab/+mom/default_option_mom_values.m
+++ b/matlab/+mom/default_option_mom_values.m
@@ -318,6 +318,11 @@ options_mom_.varobs = options_.varobs; % observable variables in or
options_mom_.varobs_id = options_.varobs_id; % index for observable variables in M_.endo_names
options_mom_.obs_nbr = length(options_mom_.varobs); % number of observed variables
+%related to discretionary policy
+options_mom_.discretionary_policy = options_.discretionary_policy; % whether discretionary policy is used
+options_mom_.discretionary_tol = options_.discretionary_tol; % tolerance of discretionary policy
+options_mom_.dp = options_.dp; % other options
+
% related to call of dynare
options_mom_.console_mode = options_.console_mode;
if options_mom_.console_mode
diff --git a/matlab/+mom/objective_function.m b/matlab/+mom/objective_function.m
index 98247f51cbc698c2c0823fffd88afc6542577c6f..bd14210163f6767f771cff1e43c65227c869b3ec 100644
--- a/matlab/+mom/objective_function.m
+++ b/matlab/+mom/objective_function.m
@@ -105,10 +105,10 @@ end
%--------------------------------------------------------------------------
-% Call resol to compute steady state and model solution
+% Compute steady state and model solution
%--------------------------------------------------------------------------
% Compute linear approximation around the deterministic steady state
-[dr, info, M_.params] = resol(0, M_, options_mom_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
+[dr, info, M_.params] = compute_decision_rules(M_,options_mom_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
% Return, with endogenous penalty when possible, if resol issues an error code
if info(1)
if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 ||...
diff --git a/meson.build b/meson.build
index c0a20081670c758e85f0ed611c0287eb214476d7..ea1151ed27b308210f2aa024eb27ee8606a1f374 100644
--- a/meson.build
+++ b/meson.build
@@ -1040,7 +1040,8 @@ mod_and_m_tests = [
{ 'test' : [ 'optimal_policy/RamseyConstraints/test1.mod' ] },
{ 'test' : [ 'optimal_policy/Ramsey/Ramsey_Example_estimation.mod' ] },
{ 'test' : [ 'discretionary_policy/dennis_1.mod',
- 'discretionary_policy/dennis_1_estim.mod' ] },
+ 'discretionary_policy/dennis_1_estim.mod',
+ 'discretionary_policy/dennis_1_estim_MoM.mod' ] },
{ 'test' : [ 'discretionary_policy/Gali_discretion.mod' ] },
{ 'test' : [ 'discretionary_policy/Gali_2015_chapter_3.mod',
'discretionary_policy/Gali_2015_chapter_3_nonlinear.mod' ] },
diff --git a/tests/discretionary_policy/dennis_1_estim_MoM.mod b/tests/discretionary_policy/dennis_1_estim_MoM.mod
new file mode 100644
index 0000000000000000000000000000000000000000..8e77129df9a90e63854c261e4413f5f00268f78d
--- /dev/null
+++ b/tests/discretionary_policy/dennis_1_estim_MoM.mod
@@ -0,0 +1,70 @@
+/* Test for estimation under discretionary optimal policy.
+ Uses the data generated by dennis_1.mod */
+
+var y i pi pi_c q;
+varexo g u e;
+
+parameters omega kappa sigma beta alpha;
+
+alpha = 0.4;
+beta = 0.99;
+sigma = 1;
+omega = 0.9;
+kappa = 0.3;
+
+model(linear);
+y = y(+1) -(omega/sigma)*(i-pi(+1))+g;
+pi = beta*pi(+1)+kappa*y+u;
+pi_c = pi+(alpha/(1-alpha))*(q-q(-1));
+q = q(+1)-(1-alpha)*(i-pi(+1))+(1-alpha)*e;
+end;
+
+shocks;
+ var g; stderr 1;
+ var u; stderr 1;
+ var e; stderr 1;
+end;
+
+planner_objective pi_c^2 + y^2;
+discretionary_policy(instruments=(i),irf=0,planner_discount=beta);
+
+varobs y i pi;
+
+estimated_params;
+ omega, normal_pdf, 0.9, 0.1;
+ kappa, normal_pdf, 0.2, 0.1;
+end;
+
+
+matched_moments;
+y;
+pi;
+y*y;
+pi*pi;
+y*y(-1);
+pi*pi(-1);
+y*y(-3);
+pi*pi(-3);
+end;
+
+method_of_moments(
+ % Necessary options
+ mom_method = GMM % method of moments method; possible values: GMM|SMM
+ , datafile = 'dennis_simul.m' % name of filename with data
+
+ , order = 1 % order of Taylor approximation in perturbation
+ % , penalized_estimator % include deviation from prior mean as additional moment restriction and use prior precision as weight
+ % , pruning % use pruned state space system at higher-order
+ % , verbose % display and store intermediate estimation results
+ , weighting_matrix = ['DIAGONAL','OPTIMAL'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename. Size of cell determines stages in iterated estimation, e.g. two state with ['DIAGONAL','OPTIMAL']
+ % , weighting_matrix_scaling_factor=1 % scaling of weighting matrix in objective function
+ , se_tolx=1e-6 % step size for numerical computation of standard errors
+ , TeX % print TeX tables and graphics
+ , mode_compute = 3 % specifies the optimizer for minimization of moments distance
+ , additional_optimizer_steps = [13] % vector of additional mode-finders run after mode_compute
+ , optim = ('TolFun' , 1D-6 % termination tolerance on the function value, a positive scalar
+ ,'TolX' , 1e-6 % termination tolerance on x, a positive scalar
+ )
+ , silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
+ , mode_check % plot the target function for values around the computed minimum for each estimated parameter in turn
+ );
\ No newline at end of file