diff --git a/matlab/evaluate_planner_objective.m b/matlab/evaluate_planner_objective.m
index cbaf1b4d090f1c73b24db8b393aa19d011d1ce55..ec34a87b69d7ab711c9994a765b7b0441e6795fd 100644
--- a/matlab/evaluate_planner_objective.m
+++ b/matlab/evaluate_planner_objective.m
@@ -6,8 +6,8 @@ function planner_objective_value = evaluate_planner_objective(M_,options_,oo_)
% oo_: (structure) output results
% OUTPUT
% planner_objective_value (double)
-%
-%Returns a vector containing first order or second-order approximations of
+%
+%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.
@@ -52,7 +52,7 @@ function planner_objective_value = evaluate_planner_objective(M_,options_,oo_)
% 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.
+% 1) the second-order derivatives of the policy and transition functions h and g are zero.
% 2) the unconditional expectation of states coincides with its steady-state, which entails E(yhat) = 0
% Therefore, the unconditional welfare can now be approximated as
% E(W) = (1 - beta)^{-1} ( Ubar + 0.5 ( U_xx h_y^2 E(yhat^2) + U_xx h_u^2 E(u^2) )
@@ -91,47 +91,47 @@ planner_objective_value = zeros(2,1);
if options_.ramsey_policy
if oo_.gui.ran_perfect_foresight
T = size(oo_.endo_simul,2);
- [U_term] = feval([M_.fname '.objective.static'],oo_.endo_simul(:,T),oo_.exo_simul(T,:), M_.params);
+ [U_term] = feval([M_.fname '.objective.static'],oo_.endo_simul(:,T-M_.maximum_lead),oo_.exo_simul(T-M_.maximum_lead,:), M_.params);
EW = U_term/(1-beta);
W = EW;
- for t=T:-1:2
+ for t=T-M_.maximum_lead:-1:1+M_.maximum_lag
[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;
else
- ys = oo_.dr.ys;
+ 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);
+ planner_objective_value(2) = U/(1-beta);
elseif options_.order == 2 && ~M_.hessian_eq_zero
[U,Uy,Uyy] = feval([M_.fname '.objective.static'],ys,zeros(1,exo_nbr), M_.params);
-
+
Gy = dr.ghx(nstatic+(1:nspred),:);
Gu = dr.ghu(nstatic+(1:nspred),:);
Gyy = dr.ghxx(nstatic+(1:nspred),:);
Gyu = dr.ghxu(nstatic+(1:nspred),:);
Guu = dr.ghuu(nstatic+(1:nspred),:);
Gss = dr.ghs2(nstatic+(1:nspred),:);
-
+
gy(dr.order_var,:) = dr.ghx;
gu(dr.order_var,:) = dr.ghu;
gyy(dr.order_var,:) = dr.ghxx;
gyu(dr.order_var,:) = dr.ghxu;
guu(dr.order_var,:) = dr.ghuu;
gss(dr.order_var,:) = dr.ghs2;
-
+
Uyy = full(Uyy);
-
+
Uyygygy = A_times_B_kronecker_C(Uyy,gy,gy);
Uyygugu = A_times_B_kronecker_C(Uyy,gu,gu);
-
+
%% Unconditional welfare
-
+
old_noprint = options_.noprint;
-
+
if ~old_noprint
options_.noprint = 1;
end
@@ -143,25 +143,25 @@ if options_.ramsey_policy
if ~old_noprint
options_.noprint = 0;
end
-
+
oo_.mean(isnan(oo_.mean)) = options_.huge_number;
oo_.var(isnan(oo_.var)) = options_.huge_number;
-
+
Ey = oo_.mean;
Eyhat = Ey - ys(dr.order_var(nstatic+(1:nspred)));
-
+
var_corr = Eyhat*Eyhat';
Eyhatyhat = oo_.var(:) + var_corr(:);
Euu = M_.Sigma_e(:);
-
+
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
-
+
Wbar = U/(1-beta);
Wy = Uy*gy/(eye(nspred)-beta*Gy);
-
+
if isempty(options_.qz_criterium)
options_.qz_criterium = 1+1e-6;
end
@@ -171,7 +171,7 @@ if options_.ramsey_policy
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;
else
@@ -184,23 +184,23 @@ if options_.ramsey_policy
end
elseif options_.discretionary_policy
ys = oo_.dr.ys;
-
+
[U,Uy,Uyy] = 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;
-
+
Uyy = full(Uyy);
-
+
Uyygygy = A_times_B_kronecker_C(Uyy,gy,gy);
Uyygugu = A_times_B_kronecker_C(Uyy,gu,gu);
-
+
%% Unconditional welfare
-
+
old_noprint = options_.noprint;
-
+
if ~old_noprint
options_.noprint = 1;
end
@@ -209,36 +209,33 @@ elseif options_.discretionary_policy
if ~old_noprint
options_.noprint = 0;
end
-
+
oo_.mean(isnan(oo_.mean)) = options_.huge_number;
oo_.var(isnan(oo_.var)) = options_.huge_number;
-
+
Ey = oo_.mean;
Eyhat = Ey - ys(dr.order_var(nstatic+(1:nspred)));
-
+
var_corr = Eyhat*Eyhat';
Eyhatyhat = oo_.var(:) + var_corr(:);
Euu = M_.Sigma_e(:);
-
+
EU = U + Uy*gy*Eyhat + 0.5*(Uyygygy*Eyhatyhat + Uyygugu*Euu);
EW = EU/(1-beta);
-
-
+
+
%% Conditional welfare starting from the non-stochastic steady-state
-
+
Wbar = U/(1-beta);
Wy = Uy*gy/(eye(nspred)-beta*Gy);
-
- if isempty(options_.qz_criterium)
- options_.qz_criterium = 1+1e-6;
- end
+
%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);
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;
end
@@ -250,7 +247,7 @@ if ~options_.noprint
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))
- end
+ 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))
diff --git a/tests/optimal_policy/Ramsey/ramsey_histval.mod b/tests/optimal_policy/Ramsey/ramsey_histval.mod
index 09ee4c68999e9dc5b9de2e397b5f3d4f9563d4a8..4c61ba60bf79efe5a7b6e14f8169a26566a4165e 100644
--- a/tests/optimal_policy/Ramsey/ramsey_histval.mod
+++ b/tests/optimal_policy/Ramsey/ramsey_histval.mod
@@ -23,7 +23,7 @@ r=1;
end;
histval;
-r(0)=1;
+a(0)=-1;
end;
steady_state_model;
@@ -42,4 +42,5 @@ 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,periods=500);
\ No newline at end of file
+stoch_simul(order=1);
+evaluate_planner_objective;
\ No newline at end of file