diff --git a/matlab/evaluate_planner_objective.m b/matlab/evaluate_planner_objective.m
index ec34a87b6..3a516b31b 100644
--- a/matlab/evaluate_planner_objective.m
+++ b/matlab/evaluate_planner_objective.m
@@ -5,12 +5,14 @@ function planner_objective_value = evaluate_planner_objective(M_,options_,oo_)
 %   options_:  (structure) options
 %   oo_:       (structure) output results
 % OUTPUT
-%  planner_objective_value (double)
+%  planner_objective_value (structure)
 %
-%Returns a vector containing first order or second-order approximations of
-% - the unconditional expectation of the planner's objective function
-% - the conditional expectation of the planner's objective function starting from the non-stochastic steady state and allowing for future shocks
-% depending on the value of options_.order.
+%Returns a structure containing approximations of
+% - the unconditional expectation of the planner's objective function in the field unconditional
+% - the conditional expectations of the planner's objective function starting from the non-stochastic steady state in the field conditional
+%   - with Lagrange multipliers initially set to zero in the field zero_initial_multiplier
+%   - with lagrange multipliers initially set to their initial values in the field steady_initial_multiplier
+% Approximations are consistent with the order specified in options_order.
 %
 % SPECIAL REQUIREMENTS
 %   none
@@ -38,8 +40,6 @@ function planner_objective_value = evaluate_planner_objective(M_,options_,oo_)
 % Taking the unconditional expectation yields E(U) = Ubar and E(W) = Ubar/(1-beta)
 % As for conditional welfare, a first-order approximation leads to
 % W = Wbar + W_y yhat_{t-1} + W_u u_t
-% The approximated conditional expectation of the planner's objective function taking at the non-stochastic steady-state and allowing for future shocks thus verifies
-% W (y, 0, 1) = Wbar
 
 % Similarly, taking the unconditional expectation of a second-order approximation of utility around the non-stochastic steady state yields a second-order approximation of unconditional welfare
 % E(W) = (1 - beta)^{-1} ( Ubar + U_x h_y E(yhat) + 0.5 ( (U_xx h_y^2 + U_x h_yy) E(yhat^2) + (U_xx h_u^2 + U_x h_uu) E(u^2) + U_x h_ss )
@@ -48,8 +48,6 @@ function planner_objective_value = evaluate_planner_objective(M_,options_,oo_)
 % As for conditional welfare, the second-order approximation of welfare around the non-stochastic steady state leads to
 % W(y_{t-1}, u_t, sigma) = Wbar + W_y yhat_{t-1} + W_u u_t + W_yu yhat_{t-1} ⊗ u_t + 0.5 ( W_yy yhat_{t-1}^2 + W_uu u_t^2 + W_ss )
 % The derivatives of W taken at the non-stochastic steady state can be computed as in Kamenik and Juillard (2004) "Solving Stochastic Dynamic Equilibrium Models: A k-Order Perturbation Approach".
-% The approximated conditional expectation of the planner's objective function starting from the non-stochastic steady-state and allowing for future shocks thus verifies
-% W(y,0,1) = Wbar + 0.5*Wss
 
 % In the discretionary case, the model is assumed to be linear and the utility is assumed to be linear-quadratic. This changes 2 aspects of the results delinated above:
 % 1) the second-order derivatives of the policy and transition functions h and g are zero.
@@ -87,7 +85,6 @@ nstatic = M_.nstatic;
 nspred = M_.nspred;
 beta = get_optimal_policy_discount_factor(M_.params, M_.param_names);
 
-planner_objective_value = zeros(2,1);
 if options_.ramsey_policy
     if oo_.gui.ran_perfect_foresight
         T = size(oo_.endo_simul,2);
@@ -98,14 +95,39 @@ if options_.ramsey_policy
             [U] = feval([M_.fname '.objective.static'],oo_.endo_simul(:,t),oo_.exo_simul(t,:), M_.params);
             W = U + beta*W;
         end
-        planner_objective_value(1) = EW;
-        planner_objective_value(2) = W;
+        planner_objective_value = struct("conditional", W, "unconditional", EW);
     else
+        planner_objective_value = struct("conditional", struct("zero_initial_multiplier", 0., "steady_initial_multiplier", 0.), "unconditional", 0.);
         ys = oo_.dr.ys;
         if options_.order == 1 || M_.hessian_eq_zero
-            [U] = feval([M_.fname '.objective.static'],ys,zeros(1,exo_nbr), M_.params);
-            planner_objective_value(1) = U/(1-beta);
-            planner_objective_value(2) = U/(1-beta);
+            [U,Uy] = feval([M_.fname '.objective.static'],ys,zeros(1,exo_nbr), M_.params);
+
+            Gy = dr.ghx(nstatic+(1:nspred),:);
+            Gu = dr.ghu(nstatic+(1:nspred),:);
+
+            gy(dr.order_var,:) = dr.ghx;
+            gu(dr.order_var,:) = dr.ghu;
+
+            %% Unconditional welfare
+
+            EW = U/(1-beta);
+            
+            planner_objective_value.unconditional = EW;
+
+            %% Conditional welfare starting from the non-stochastic steady-state (i) with Lagrange multipliers set to their steady-state value (ii) with Lagrange multipliers set to 0
+            
+            Wbar = U/(1-beta);
+            Wy = Uy*gy/(eye(nspred)-beta*Gy);
+            Wu = Uy*gu + beta*Wy*Gu;
+            
+            [yhat_L_SS,yhat_L_0, u]=get_initial_state(ys,M_,dr,oo_);
+
+            W_L_SS = Wbar+Wy*yhat_L_SS+Wu*u;
+            W_L_0 = Wbar+Wy*yhat_L_0+Wu*u;
+          
+            planner_objective_value.conditional.steady_initial_multiplier = W_L_SS;
+            planner_objective_value.conditional.zero_initial_multiplier = W_L_0;
+
         elseif options_.order == 2 && ~M_.hessian_eq_zero
             [U,Uy,Uyy] = feval([M_.fname '.objective.static'],ys,zeros(1,exo_nbr), M_.params);
             
@@ -127,6 +149,7 @@ if options_.ramsey_policy
             
             Uyygygy = A_times_B_kronecker_C(Uyy,gy,gy);
             Uyygugu = A_times_B_kronecker_C(Uyy,gu,gu);
+            Uyygugy = A_times_B_kronecker_C(Uyy,gu,gy);
             
             %% Unconditional welfare
             
@@ -157,10 +180,13 @@ if options_.ramsey_policy
             EU = U + Uy*gy*Eyhat + 0.5*((Uyygygy + Uy*gyy)*Eyhatyhat + (Uyygugu + Uy*guu)*Euu + Uy*gss);
             EW = EU/(1-beta);
             
-            %% Conditional welfare starting from the non-stochastic steady-state
-            
+            planner_objective_value.unconditional = EW;
+
+            %% Conditional welfare starting from the non-stochastic steady-state (i) with Lagrange multipliers set to their steady-state value (ii) with Lagrange multipliers set to 0
+             
             Wbar = U/(1-beta);
             Wy = Uy*gy/(eye(nspred)-beta*Gy);
+            Wu = Uy*gu + beta*Wy*Gu;
             
             if isempty(options_.qz_criterium)
                 options_.qz_criterium = 1+1e-6;
@@ -168,23 +194,42 @@ if options_.ramsey_policy
             %solve Lyapunuv equation Wyy=gy'*Uyy*gy+Uy*gyy+beta*Wy*Gyy+beta*Gy'Wyy*Gy
             Wyy = reshape(lyapunov_symm(sqrt(beta)*Gy',reshape(Uyygygy + Uy*gyy + beta*Wy*Gyy,nspred,nspred),options_.lyapunov_fixed_point_tol,options_.qz_criterium,options_.lyapunov_complex_threshold, 3, options_.debug),1,nspred*nspred);
             Wyygugu = A_times_B_kronecker_C(Wyy,Gu,Gu);
+            Wyygugy = A_times_B_kronecker_C(Wyy,Gu,Gy);
             Wuu = Uyygugu + Uy*guu + beta*(Wyygugu + Wy*Guu);
             Wss = (Uy*gss + beta*(Wy*Gss + Wuu*M_.Sigma_e(:)))/(1-beta);
-            W = Wbar + 0.5*Wss;
-            
-            planner_objective_value(1) = EW;
-            planner_objective_value(2) = W;
+            Wyu = Uyygugy + Uy*gyu + beta*(Wyygugy + Wy*Gyu);
+
+            [yhat_L_SS,yhat_L_0, u]=get_initial_state(ys,M_,dr,oo_);
+
+            Wyu_yu_L_SS = A_times_B_kronecker_C(Wyu,yhat_L_SS,u);
+            Wyy_yy_L_SS = A_times_B_kronecker_C(Wyy,yhat_L_SS,yhat_L_SS);
+            Wuu_uu_L_SS = A_times_B_kronecker_C(Wuu,u,u);
+            W_L_SS = Wbar+Wy*yhat_L_SS+Wu*u+Wyu_yu_L_SS+0.5*(Wss+Wyy_yy_L_SS+Wuu_uu_L_SS);
+
+            Wyu_yu_L_0 = A_times_B_kronecker_C(Wyu,yhat_L_0,u);
+            Wyy_yy_L_0 = A_times_B_kronecker_C(Wyy,yhat_L_0,yhat_L_0);
+            Wuu_uu_L_0 = A_times_B_kronecker_C(Wuu,u,u);
+            W_L_0 = Wbar+Wy*yhat_L_0+Wu*u+Wyu_yu_L_0+0.5*(Wss+Wyy_yy_L_0+Wuu_uu_L_0);
+          
+            planner_objective_value.conditional.steady_initial_multiplier = W_L_SS;
+            planner_objective_value.conditional.zero_initial_multiplier = W_L_0;
         else
             %Order k code will go here!
-            fprintf('\nevaluate_planner_objective: order>2 unconditional welfare calculation not yet supported\n')
-            planner_objective_value(1) = k_order_welfare(dr, M_, options_);
-            planner_objective_value(2) = NaN;
+            if ~isempty(M_.endo_histval)
+               fprintf('\nevaluate_planner_objective: order>2 conditional and unconditional welfare calculations not yet supported when an histval block is provided\n')
+            else
+               fprintf('\nevaluate_planner_objective: order>2 conditional welfare with initial Lagrange multipliers set to zero and unconditional welfare calculations not yet supported\n')
+               planner_objective_value.conditional.steady_initial_multiplier = k_order_welfare(dr, M_, options_);
+               planner_objective_value.conditional.zero_initial_multiplier = NaN;
+               planner_objective_value.unconditional = NaN;
+            end
             return
         end
     end
 elseif options_.discretionary_policy
     ys = oo_.dr.ys;
-    
+    planner_objective_value = struct("conditional", struct("zero_initial_multiplier", 0., "steady_initial_multiplier", 0.), "unconditional", 0.);
+
     [U,Uy,Uyy] = feval([M_.fname '.objective.static'],ys,zeros(1,exo_nbr), M_.params);
     
     Gy = dr.ghx(nstatic+(1:nspred),:);
@@ -196,6 +241,7 @@ elseif options_.discretionary_policy
     
     Uyygygy = A_times_B_kronecker_C(Uyy,gy,gy);
     Uyygugu = A_times_B_kronecker_C(Uyy,gu,gu);
+    Uyygugy = A_times_B_kronecker_C(Uyy,gu,gy);
     
     %% Unconditional welfare
     
@@ -222,34 +268,99 @@ elseif options_.discretionary_policy
     
     EU = U + Uy*gy*Eyhat + 0.5*(Uyygygy*Eyhatyhat + Uyygugu*Euu);
     EW = EU/(1-beta);
-    
-    
+    planner_objective_value.unconditional = EW;
+
     %% Conditional welfare starting from the non-stochastic steady-state
     
     Wbar = U/(1-beta);
     Wy = Uy*gy/(eye(nspred)-beta*Gy);
-    
+    Wu = Uy*gu + beta*Wy*Gu;
+
     %solve Lyapunuv equation Wyy=gy'*Uyy*gy+beta*Gy'Wyy*Gy
     Wyy = reshape(lyapunov_symm(sqrt(beta)*Gy',reshape(Uyygygy,nspred,nspred),options_.lyapunov_fixed_point_tol,options_.qz_criterium,options_.lyapunov_complex_threshold, 3, options_.debug),1,nspred*nspred);
     Wyygugu = A_times_B_kronecker_C(Wyy,Gu,Gu);
+    Wyygugy = A_times_B_kronecker_C(Wyy,Gu,Gy);
     Wuu = Uyygugu + beta*Wyygugu;
     Wss = beta*Wuu*M_.Sigma_e(:)/(1-beta);
-    W = Wbar + 0.5*Wss;
-    
-    planner_objective_value(1) = EW;
-    planner_objective_value(2) = W;
+    Wyu = Uyygugy + beta*Wyygugy;
+
+    [yhat_L_SS,yhat_L_0, u]=get_initial_state(ys,M_,dr,oo_);
+
+    Wyu_yu_L_SS = A_times_B_kronecker_C(Wyu,yhat_L_SS,u);
+    Wyy_yy_L_SS = A_times_B_kronecker_C(Wyy,yhat_L_SS,yhat_L_SS);
+    Wuu_uu_L_SS = A_times_B_kronecker_C(Wuu,u,u);
+    W_L_SS = Wbar+Wy*yhat_L_SS+Wu*u+Wyu_yu_L_SS+0.5*(Wss+Wyy_yy_L_SS+Wuu_uu_L_SS);
+
+    Wyu_yu_L_0 = A_times_B_kronecker_C(Wyu,yhat_L_0,u);
+    Wyy_yy_L_0 = A_times_B_kronecker_C(Wyy,yhat_L_0,yhat_L_0);
+    Wuu_uu_L_0 = A_times_B_kronecker_C(Wuu,u,u);
+    W_L_0 = Wbar+Wy*yhat_L_0+Wu*u+Wyu_yu_L_0+0.5*(Wss+Wyy_yy_L_0+Wuu_uu_L_0);
+   
+    planner_objective_value.conditional.steady_initial_multiplier = W_L_SS;
+    planner_objective_value.conditional.zero_initial_multiplier = W_L_0;
 end
 if ~options_.noprint
     if options_.ramsey_policy
         if oo_.gui.ran_perfect_foresight
-            fprintf('\nSimulated value of unconditional welfare:  %10.8f\n', planner_objective_value(1))
-            fprintf('\nSimulated value of conditional welfare:  %10.8f\n', planner_objective_value(2))
+             fprintf('\nSimulated value of unconditional welfare:  %10.8f\n', planner_objective_value.unconditional)
+             fprintf('\nSimulated value of conditional welfare:  %10.8f\n', planner_objective_value.conditional)
         else
-            fprintf('\nApproximated value of unconditional welfare:  %10.8f\n', planner_objective_value(1))
-            fprintf('\nApproximated value of conditional welfare:  %10.8f\n', planner_objective_value(2))
+             fprintf('\nApproximated value of unconditional welfare:  %10.8f\n', planner_objective_value.unconditional)
+             fprintf('\nApproximated value of conditional welfare:\n')
+             fprintf('    - with initial Lagrange multipliers set to 0: %10.8f\n', planner_objective_value.conditional.zero_initial_multiplier)
+             fprintf('    - with initial Lagrange multipliers set to steady state: %10.8f\n\n', planner_objective_value.conditional.steady_initial_multiplier)
         end
     elseif options_.discretionary_policy
-        fprintf('\nApproximated value of unconditional welfare with discretionary policy:  %10.8f\n', planner_objective_value(1))
-        fprintf('\nApproximated value of conditional welfare with discretionary policy:  %10.8f\n', planner_objective_value(2))
+          fprintf('\nApproximated value of unconditional welfare with discretionary policy:  %10.8f\n', planner_objective_value.unconditional)
+          fprintf('\nApproximated value of conditional welfare with discretionary policy:\n')
+          fprintf('    - with initial Lagrange multipliers set to 0: %10.8f\n', planner_objective_value.conditional.zero_initial_multiplier)
+          fprintf('    - with initial Lagrange multipliers set to steady state: %10.8f\n\n', planner_objective_value.conditional.steady_initial_multiplier)
     end
 end
+
+function [yhat_L_SS,yhat_L_0, u]=get_initial_state(ys,M_,dr,oo_)
+
+% initialize Lagrange multipliers to their steady-state values in yhat_L_SS
+yhat_L_SS = ys;
+% initialize Lagrange multipliers to 0 in yhat_L_0
+yhat_L_0 = zeros(M_.endo_nbr,1);
+if ~isempty(M_.aux_vars)
+mult_indicator=([M_.aux_vars(:).type]==6);
+mult_indices=[M_.aux_vars(mult_indicator).endo_index];
+else
+    mult_indices=[];
+end
+non_mult_indices=~ismember(1:M_.endo_nbr,mult_indices);
+if ~isempty(M_.endo_histval)
+    % initialize endogenous state variable to histval if necessary
+    yhat_L_SS(non_mult_indices) = M_.endo_histval(non_mult_indices);
+    yhat_L_0(non_mult_indices) = M_.endo_histval(non_mult_indices);
+else
+    yhat_L_0(non_mult_indices) = ys(non_mult_indices);
+end
+yhat_L_0 = yhat_L_0(dr.order_var(M_.nstatic+(1:M_.nspred)),1)-ys(dr.order_var(M_.nstatic+(1:M_.nspred)));
+yhat_L_SS = yhat_L_SS(dr.order_var(M_.nstatic+(1:M_.nspred)),1)-ys(dr.order_var(M_.nstatic+(1:M_.nspred)));
+if ~isempty(M_.det_shocks)
+    if ~all(oo_.exo_simul(1,:)==0)
+        fprintf(['\nevaluate_planner_objective: oo_.exo_simul contains simulated values for the initial period.\n'...
+            'evaluate_planner_objective: Dynare will ignore them and use the provided initial condition.\n'])        
+    end
+    u =oo_.exo_steady_state;
+    periods=[M_.det_shocks(:).periods];
+    if any(periods==0)
+        fprintf(['\nevaluate_planner_objective: M_.det_shocks contains values for the predetermined t=0 period.\n'...
+            'evaluate_planner_objective: Dynare will ignore them. Use histval to set the value of lagged innovations.\n'])
+    end
+    if any(periods>1)
+        fprintf(['\nevaluate_planner_objective: Shock values for periods not contained in the initial information set (t=1) have been provided.\n' ...
+                  'evaluate_planner_objective: Note that they will be ignored.\n'])
+    end
+    shock_indices=find(periods==1);    
+    if any([M_.det_shocks(shock_indices).multiplicative])
+        fprintf(['\nevaluate_planner_objective: Shock values need to be specified as additive.\n'])        
+    end
+    u([M_.det_shocks(shock_indices).exo_id])=[M_.det_shocks(shock_indices).value];
+else
+    u = oo_.exo_simul(1,:)'; %first value of simulation series (set by simult.m if periods>0), 1 otherwise    
+end
+    
diff --git a/tests/Makefile.am b/tests/Makefile.am
index bc45b6105..1a38316ec 100644
--- a/tests/Makefile.am
+++ b/tests/Makefile.am
@@ -679,6 +679,9 @@ kalman_filter_smoother/algo4.o.trs: kalman_filter_smoother/algo3.o.trs
 kalman_filter_smoother/test_compute_Pinf_Pstar.m.trs: kalman_filter_smoother/test_compute_Pinf_Pstar_data.m.trs
 kalman_filter_smoother/test_compute_Pinf_Pstar.o.trs: kalman_filter_smoother/test_compute_Pinf_Pstar_data.o.trs
 
+optimal_policy/Ramsey/ramsey_ex.m.trs: optimal_policy/Ramsey/ramsey_ex_initval.m.trs
+optimal_policy/Ramsey/ramsey_ex.o.trs: optimal_policy/Ramsey/ramsey_ex_initval.o.trs
+
 optimal_policy/nk_ramsey_expectation_a.m.trs: optimal_policy/nk_ramsey_expectation.m.trs
 optimal_policy/nk_ramsey_expectation_a.o.trs: optimal_policy/nk_ramsey_expectation.o.trs
 
@@ -1527,6 +1530,8 @@ clean-local:
 
 	rm -f occbin/filter/dataobsfile2.mat
 
+	rm -f optimal_policy/Ramsey/oo_ramsey_policy_initval.mat
+
 	find . -name "*.tex" -type f -delete
 	find . -name "*.aux" -type f -delete
 	find . -name "*.log" -type f -delete
diff --git a/tests/discretionary_policy/Gali_2015_chapter_3_nonlinear.mod b/tests/discretionary_policy/Gali_2015_chapter_3_nonlinear.mod
index ca76e3033..3cc7bb649 100644
--- a/tests/discretionary_policy/Gali_2015_chapter_3_nonlinear.mod
+++ b/tests/discretionary_policy/Gali_2015_chapter_3_nonlinear.mod
@@ -1,5 +1,5 @@
 /*
- * This file implements the baseline New Keynesian model of Jordi Galí (2015): Monetary Policy, Inflation,
+ * This file implements the baseline New Keynesian model of Jordi Gal� (2015): Monetary Policy, Inflation,
  * and the Business Cycle, Princeton University Press, Second Edition, Chapter 3
  *
  * Note that this mod-file implements the non-linear first order conditions and that the IRFs show the log-deviations
@@ -220,8 +220,10 @@ planner_objective 0.5*((siggma+(varphi+alppha)/(1-alppha))*y_hat^2+epsilon/0.021
 discretionary_policy(order=1,instruments=(R),irf=20,planner_discount=betta, periods=0) y_hat pi_ann log_y log_N log_W_real log_P;
 
 temp=load(['Gali_2015_chapter_3' filesep 'Output' filesep 'Gali_2015_chapter_3_results.mat']);
-if abs(oo_.planner_objective_value-temp.oo_.planner_objective_value)>1e-6
-    warning('Planner objective does not match linear model')
+if abs(oo_.planner_objective_value.unconditional-temp.oo_.planner_objective_value.unconditional)>1e-6 ... 
+    || abs(oo_.planner_objective_value.conditional.zero_initial_multiplier-temp.oo_.planner_objective_value.conditional.zero_initial_multiplier)>1e-6 ... 
+    ||  abs(oo_.planner_objective_value.conditional.steady_initial_multiplier-temp.oo_.planner_objective_value.conditional.steady_initial_multiplier)>1e-6 ... 
+   warning('Planner objective does not match linear model')
 end
 if max(max(abs([temp.oo_.irfs.y_eps_a; temp.oo_.irfs.w_real_eps_a; temp.oo_.irfs.n_eps_a; temp.oo_.irfs.pi_ann_eps_a]-...
         [oo_.irfs.log_y_eps_a; oo_.irfs.log_W_real_eps_a; oo_.irfs.log_N_eps_a; oo_.irfs.pi_ann_eps_a])))>1e-6
diff --git a/tests/discretionary_policy/Gali_discretion.mod b/tests/discretionary_policy/Gali_discretion.mod
index a368462b1..d04dea02e 100644
--- a/tests/discretionary_policy/Gali_discretion.mod
+++ b/tests/discretionary_policy/Gali_discretion.mod
@@ -130,7 +130,7 @@ end
 %Compute theoretical objective function
 V=betta/(1-betta)*(var_pi_theoretical+alpha_x*var_y_gap_theoretical); %evaluate at steady state in first period
 
-if any( [ isnan(oo_.planner_objective_value(2)), abs(V-oo_.planner_objective_value(2))>1e-10 ] )
+if any( [ isnan(oo_.planner_objective_value.conditional.zero_initial_multiplier), abs(V-oo_.planner_objective_value.conditional.zero_initial_multiplier)>1e-10 ] )
     error('Computed welfare deviates from theoretical welfare')
 end
 end;
@@ -144,6 +144,6 @@ end;
 V=var_pi_theoretical+alpha_x*var_y_gap_theoretical+ betta/(1-betta)*(var_pi_theoretical+alpha_x*var_y_gap_theoretical); %evaluate at steady state in first period
  
 discretionary_policy(instruments=(i),irf=20,discretionary_tol=1e-12,planner_discount=betta) y_gap pi p u;
-if any( [ isnan(oo_.planner_objective_value(1)), abs(V-oo_.planner_objective_value(1))>1e-10 ] )
+if any( [ isnan(oo_.planner_objective_value.conditional.steady_initial_multiplier), abs(V-oo_.planner_objective_value.conditional.steady_initial_multiplier)>1e-10 ] )
     error('Computed welfare deviates from theoretical welfare')
 end
diff --git a/tests/optimal_policy/Ramsey/ramsey_ex.mod b/tests/optimal_policy/Ramsey/ramsey_ex.mod
index 04e124462..ba00dcacf 100644
--- a/tests/optimal_policy/Ramsey/ramsey_ex.mod
+++ b/tests/optimal_policy/Ramsey/ramsey_ex.mod
@@ -47,7 +47,9 @@ if max(abs((oo_ramsey_policy_steady_state_file.steady_state-oo_.steady_state)))>
     || max(max(abs(oo_ramsey_policy_steady_state_file.dr.ghx-oo_.dr.ghx)))>1e-5 ...
     || max(max(abs(oo_ramsey_policy_steady_state_file.dr.ghu-oo_.dr.ghu)))>1e-5 ...
     || max(max(abs(oo_ramsey_policy_steady_state_file.dr.Gy-oo_.dr.Gy)))>1e-5 ...
-    || max(abs((oo_ramsey_policy_steady_state_file.planner_objective_value-oo_.planner_objective_value)))>1e-5
+    || abs(oo_ramsey_policy_steady_state_file.planner_objective_value.unconditional-oo_.planner_objective_value.unconditional)>1e-5 ...
+    || abs(oo_ramsey_policy_steady_state_file.planner_objective_value.conditional.zero_initial_multiplier-oo_.planner_objective_value.conditional.zero_initial_multiplier)>1e-5 ...
+    || abs(oo_ramsey_policy_steady_state_file.planner_objective_value.conditional.steady_initial_multiplier-oo_.planner_objective_value.conditional.steady_initial_multiplier)>1e-5
     error('Running stoch_simul after ramsey_policy leads to inconsistent results')
 end
 
@@ -58,6 +60,8 @@ if any( [ max(abs((oo_ramsey_policy_initval.steady_state-oo_.steady_state)))>1e-
     max(max(abs(oo_ramsey_policy_initval.dr.ghx-oo_.dr.ghx)))>1e-5, ...
     max(max(abs(oo_ramsey_policy_initval.dr.ghu-oo_.dr.ghu)))>1e-5, ...
     max(max(abs(oo_ramsey_policy_initval.dr.Gy-oo_.dr.Gy)))>1e-5, ...
-    max(abs((oo_ramsey_policy_initval.planner_objective_value-oo_.planner_objective_value)))>1e-5 ] )
+    abs(oo_ramsey_policy_initval.planner_objective_value.unconditional-oo_.planner_objective_value.unconditional)>1e-5, ...
+    abs(oo_ramsey_policy_initval.planner_objective_value.conditional.zero_initial_multiplier-oo_.planner_objective_value.conditional.zero_initial_multiplier)>1e-5, ...
+    abs(oo_ramsey_policy_initval.planner_objective_value.conditional.steady_initial_multiplier-oo_.planner_objective_value.conditional.steady_initial_multiplier)>1e-5] ) 
     error('Initval and steady state file yield different results')
 end
diff --git a/tests/optimal_policy/Ramsey/ramsey_ex_initval.mod b/tests/optimal_policy/Ramsey/ramsey_ex_initval.mod
index 9f0d8e132..27f51628d 100644
--- a/tests/optimal_policy/Ramsey/ramsey_ex_initval.mod
+++ b/tests/optimal_policy/Ramsey/ramsey_ex_initval.mod
@@ -47,6 +47,8 @@ if max(abs((oo_ramsey_policy_initval.steady_state-oo_.steady_state)))>1e-5 ...
     || max(max(abs(oo_ramsey_policy_initval.dr.ghx-oo_.dr.ghx)))>1e-5 ...
     || max(max(abs(oo_ramsey_policy_initval.dr.ghu-oo_.dr.ghu)))>1e-5 ...
     || max(max(abs(oo_ramsey_policy_initval.dr.Gy-oo_.dr.Gy)))>1e-5 ...
-    || max(abs((oo_ramsey_policy_initval.planner_objective_value-oo_.planner_objective_value)))>1e-5
+    || abs(oo_ramsey_policy_initval.planner_objective_value.unconditional-oo_.planner_objective_value.unconditional)>1e-5 ...
+    || abs(oo_ramsey_policy_initval.planner_objective_value.conditional.zero_initial_multiplier-oo_.planner_objective_value.conditional.zero_initial_multiplier)>1e-5 ...
+    || abs(oo_ramsey_policy_initval.planner_objective_value.conditional.steady_initial_multiplier-oo_.planner_objective_value.conditional.steady_initial_multiplier)>1e-5
     error('Running stoch_simul after ramsey_policy leads to inconsistent results')
 end
diff --git a/tests/optimal_policy/Ramsey/ramsey_ex_initval_AR2.mod b/tests/optimal_policy/Ramsey/ramsey_ex_initval_AR2.mod
index c10204922..0c522f57f 100644
--- a/tests/optimal_policy/Ramsey/ramsey_ex_initval_AR2.mod
+++ b/tests/optimal_policy/Ramsey/ramsey_ex_initval_AR2.mod
@@ -45,6 +45,8 @@ if max(abs((oo_ramsey_policy_initval_AR2.steady_state-oo_.steady_state)))>1e-5 .
     || max(max(abs(oo_ramsey_policy_initval_AR2.dr.ghx-oo_.dr.ghx)))>1e-5 ...
     || max(max(abs(oo_ramsey_policy_initval_AR2.dr.ghu-oo_.dr.ghu)))>1e-5 ...
     || max(max(abs(oo_ramsey_policy_initval_AR2.dr.Gy-oo_.dr.Gy)))>1e-5 ...
-    || max(abs((oo_ramsey_policy_initval_AR2.planner_objective_value-oo_.planner_objective_value)))>1e-5
+    || abs(oo_ramsey_policy_initval_AR2.planner_objective_value.unconditional-oo_.planner_objective_value.unconditional)>1e-5 ...
+    || abs(oo_ramsey_policy_initval_AR2.planner_objective_value.conditional.zero_initial_multiplier-oo_.planner_objective_value.conditional.zero_initial_multiplier)>1e-5 ...
+    || abs(oo_ramsey_policy_initval_AR2.planner_objective_value.conditional.steady_initial_multiplier-oo_.planner_objective_value.conditional.steady_initial_multiplier)>1e-5
     error('Running stoch_simul after ramsey_policy leads to inconsistent results')
 end
\ No newline at end of file
diff --git a/tests/optimal_policy/Ramsey/ramsey_histval.mod b/tests/optimal_policy/Ramsey/ramsey_histval.mod
index 4c61ba60b..5f296a740 100644
--- a/tests/optimal_policy/Ramsey/ramsey_histval.mod
+++ b/tests/optimal_policy/Ramsey/ramsey_histval.mod
@@ -12,7 +12,7 @@ phi=1;
 rho=0.95;
 
 model;
-a = rho*a(-1)+u;
+a = rho*a(-1)+u+u(-1);
 1/c = beta*r/(c(+1)*pai(+1));
 pai*(pai-1)/c = beta*pai(+1)*(pai(+1)-1)/c(+1)+epsilon*phi*n^(gamma+1)/omega -exp(a)*n*(epsilon-1)/(omega*c);
 exp(a)*n = c+(omega/2)*(pai-1)^2;
@@ -23,6 +23,7 @@ r=1;
 end;
 
 histval;
+u(0)=1;
 a(0)=-1;
 end;
 
@@ -36,11 +37,30 @@ end;
 shocks;
 var u; stderr 0.008;
 var u;
-periods 1;
-values 1;
+periods 0, 1;
+values 1, 1;
 end;
 options_.dr_display_tol=0;
 planner_objective(ln(c)-phi*((n^(1+gamma))/(1+gamma)));
 ramsey_model(planner_discount=0.99,instruments=(r));
 stoch_simul(order=1);
-evaluate_planner_objective;
\ No newline at end of file
+evaluate_planner_objective;
+stoch_simul(order=2);
+evaluate_planner_objective;
+
+
+
+initval;
+r=1;
+end;
+
+histval;
+u(0)=0;
+a(0)=0;
+end;
+
+shocks(overwrite);
+var u; stderr 0.008;
+end;
+stoch_simul(order=1);
+evaluate_planner_objective;
diff --git a/tests/optimal_policy/neo_growth_ramsey.mod b/tests/optimal_policy/neo_growth_ramsey.mod
index 8185ca2f4..09ef3b9e4 100644
--- a/tests/optimal_policy/neo_growth_ramsey.mod
+++ b/tests/optimal_policy/neo_growth_ramsey.mod
@@ -45,11 +45,20 @@ unc_W_hand = oo1.oo_.mean(strmatch('W',M1.M_.endo_names,'exact'));
 initial_condition_states = repmat(oo1.oo_.dr.ys,1,M1.M_.maximum_lag);
 shock_matrix = zeros(1,M1.M_.exo_nbr);
 y_sim = simult_(M1.M_,options1.options_,initial_condition_states,oo1.oo_.dr,shock_matrix,options1.options_.order);
-cond_W_hand=y_sim(strmatch('W',M1.M_.endo_names,'exact'),2);
+cond_W_hand_L_SS=y_sim(strmatch('W',M1.M_.endo_names,'exact'),2);
 
-if abs((unc_W_hand - planner_objective_value(1))/unc_W_hand) > 1e-6;
+if abs((unc_W_hand - planner_objective_value.unconditional)/unc_W_hand) > 1e-6;
    error('Inaccurate unconditional welfare assessment');
 end;
-if abs((cond_W_hand - planner_objective_value(2))/cond_W_hand) > 1e-6;
-   error('Inaccurate conditional welfare assessment');
+if abs(cond_W_hand_L_SS - planner_objective_value.conditional.steady_initial_multiplier) > 1e-6;
+   error('Inaccurate conditional welfare with Lagrange multiplier set to its steady-state value');
+end;
+
+initial_condition_states = zeros(M1.M_.endo_nbr,M1.M_.maximum_lag);
+initial_condition_states(1:M1.M_.orig_endo_nbr,:) = repmat(oo1.oo_.dr.ys(1:M1.M_.orig_endo_nbr),1,M1.M_.maximum_lag);
+shock_matrix = zeros(1,M1.M_.exo_nbr);
+y_sim = simult_(M1.M_,options1.options_,initial_condition_states,oo1.oo_.dr,shock_matrix,options1.options_.order);
+cond_W_hand_L_0=y_sim(strmatch('W',M1.M_.endo_names,'exact'),2);
+if abs(cond_W_hand_L_0 - planner_objective_value.conditional.zero_initial_multiplier) > 1e-6;
+   error('Inaccurate conditional welfare with zero Lagrange multiplier');
 end;
diff --git a/tests/optimal_policy/neo_growth_ramsey_foresight.mod b/tests/optimal_policy/neo_growth_ramsey_foresight.mod
index b4175f58f..ef32f9b17 100644
--- a/tests/optimal_policy/neo_growth_ramsey_foresight.mod
+++ b/tests/optimal_policy/neo_growth_ramsey_foresight.mod
@@ -45,9 +45,9 @@ options1 = load(['neo_growth_foresight' filesep 'Output' filesep 'neo_growth_for
 cond_W_hand = oo1.oo_.endo_simul(strmatch('W',M1.M_.endo_names,'exact'),2);
 unc_W_hand = oo1.oo_.endo_simul(strmatch('W',M1.M_.endo_names,'exact'),end);
 
-if abs((unc_W_hand - planner_objective_value(1))/unc_W_hand) > 1e-6;
+if abs((unc_W_hand - planner_objective_value.unconditional)/unc_W_hand) > 1e-6;
    error('Inaccurate unconditional welfare assessment');
 end;
-if abs((cond_W_hand - planner_objective_value(2))/cond_W_hand) > 1e-6;
+if abs((cond_W_hand - planner_objective_value.conditional)/cond_W_hand) > 1e-6;
    error('Inaccurate conditional welfare assessment');
 end;
diff --git a/tests/optimal_policy/neo_growth_ramsey_k_order.mod b/tests/optimal_policy/neo_growth_ramsey_k_order.mod
index 02a82d48b..8e62a4399 100644
--- a/tests/optimal_policy/neo_growth_ramsey_k_order.mod
+++ b/tests/optimal_policy/neo_growth_ramsey_k_order.mod
@@ -35,8 +35,8 @@ evaluate_planner_objective;
 
 [condWelfare, U_dynpp, W_dynpp, U_dyn, W_dyn] = k_order_welfare(oo_.dr, M_, options_);
 
-if condWelfare~=oo_.planner_objective_value(1)
-    error('Values do not match');
+if condWelfare~=oo_.planner_objective_value.conditional.steady_initial_multiplier
+   error('Inaccurate conditional welfare with Lagrange multiplier set to its steady-state value');
 end
 if ~exist(['neo_growth_k_order' filesep 'Output' filesep 'neo_growth_k_order_results.mat'],'file');
    error('neo_growth_k_order must be run first');
diff --git a/tests/optimal_policy/nk_ramsey_expectation_a.mod b/tests/optimal_policy/nk_ramsey_expectation_a.mod
index 4a0f85aaf..d8eaf4684 100644
--- a/tests/optimal_policy/nk_ramsey_expectation_a.mod
+++ b/tests/optimal_policy/nk_ramsey_expectation_a.mod
@@ -100,7 +100,9 @@ end
 if (norm(o1.oo_.dr.ghu-oo_.dr.ghu,inf) > 1e-12)
    error('ghu doesn''t match')
 end
-if (abs(o1.oo_.planner_objective_value(1)-oo_.planner_objective_value(1)) > 1e-12)
+if (abs(o1.oo_.planner_objective_value.conditional.zero_initial_multiplier-oo_.planner_objective_value.conditional.zero_initial_multiplier) > 1e-12 ... 
+   || abs(o1.oo_.planner_objective_value.conditional.steady_initial_multiplier-oo_.planner_objective_value.conditional.steady_initial_multiplier) > 1e-12 ... 
+   || abs(o1.oo_.planner_objective_value.unconditional-oo_.planner_objective_value.unconditional) > 1e-12)
    error('planner objective value doesn''t match')
 end