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evaluate_planner_objective: use the sparse representation of the static model

dprior
Sébastien Villemot 2024-02-06 17:17:27 +01:00
parent d5cffba8fc
commit 28fc9e7c78
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GPG Key ID: 2CECE9350ECEBE4A
1 changed files with 14 additions and 7 deletions
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21
matlab/optimal_policy/evaluate_planner_objective.m
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@ -59,7 +59,7 @@ function planner_objective_value = evaluate_planner_objective(M_,options_,oo_)
% In the deterministic case, resorting to approximations for welfare is no longer required as it is possible to simulate the model given initial conditions for pre-determined variables and terminal conditions for forward-looking variables, whether these initial and terminal conditions are explicitly or implicitly specified. Assuming that the number of simulated periods is high enough for the new steady-state to be reached, the new unconditional welfare is thus the last period's welfare. As for the conditional welfare, it can be derived using backward recursions on the equation W = U + beta*W(+1) starting from the final unconditional steady-state welfare.
% Copyright © 2007-2022 Dynare Team
% Copyright © 2007-2024 Dynare Team
%
% This file is part of Dynare.
%
@ -92,11 +92,11 @@ end
if options_.ramsey_policy && oo_.gui.ran_perfect_foresight
T = size(oo_.endo_simul,2);
[U_term] = feval([M_.fname '.objective.static'],oo_.endo_simul(:,T-M_.maximum_lead),oo_.exo_simul(T-M_.maximum_lead,:), M_.params);
U_term = feval([M_.fname '.objective.sparse.static_resid'], 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-M_.maximum_lead:-1:1+M_.maximum_lag
[U] = feval([M_.fname '.objective.static'],oo_.endo_simul(:,t),oo_.exo_simul(t,:), M_.params);
U = feval([M_.fname '.objective.sparse.static_resid'], oo_.endo_simul(:,t), oo_.exo_simul(t,:), M_.params);
W = U + beta*W;
end
planner_objective_value = struct('conditional', W, 'unconditional', EW);
@ -108,7 +108,8 @@ else
ys = oo_.dr.ys;
end
if options_.order == 1 && ~options_.discretionary_policy
[U,Uy] = feval([M_.fname '.objective.static'],ys,zeros(1,exo_nbr), M_.params);
[U, T_order, T] = feval([M_.fname '.objective.sparse.static_resid'], ys, zeros(1,exo_nbr), M_.params);
Uy = feval([M_.fname '.objective.sparse.static_g1'], ys, zeros(1,exo_nbr), M_.params, M_.objective_g1_sparse_rowval, M_.objective_g1_sparse_colval, M_.objective_g1_sparse_colptr, T_order, T);
Gy = dr.ghx(nstatic+(1:nspred),:);
Gu = dr.ghu(nstatic+(1:nspred),:);
@ -137,7 +138,9 @@ else
planner_objective_value.conditional.zero_initial_multiplier = W_L_0;
elseif options_.order == 2 && ~M_.hessian_eq_zero %full second order approximation
[U,Uy,Uyy] = feval([M_.fname '.objective.static'],ys,zeros(1,exo_nbr), M_.params);
[U, T_order, T] = feval([M_.fname '.objective.sparse.static_resid'], ys, zeros(1,exo_nbr), M_.params);
[Uy, T_order, T] = feval([M_.fname '.objective.sparse.static_g1'], ys, zeros(1,exo_nbr), M_.params, M_.objective_g1_sparse_rowval, M_.objective_g1_sparse_colval, M_.objective_g1_sparse_colptr, T_order, T);
Uyy_v = feval([M_.fname '.objective.sparse.static_g2'], ys, zeros(1,exo_nbr), M_.params, T_order, T);
Gy = dr.ghx(nstatic+(1:nspred),:);
Gu = dr.ghu(nstatic+(1:nspred),:);
@ -153,6 +156,7 @@ else
guu(dr.order_var,:) = dr.ghuu;
gss(dr.order_var,:) = dr.ghs2;
Uyy = build_two_dim_hessian(M_.objective_g2_sparse_indices, Uyy_v, 1, M_.endo_nbr);
Uyy = full(Uyy);
Uyygygy = A_times_B_kronecker_C(Uyy,gy,gy);
@ -228,13 +232,16 @@ else
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) || options_.discretionary_policy %linear quadratic problem
[U,Uy,Uyy] = feval([M_.fname '.objective.static'],ys,zeros(1,exo_nbr), M_.params);
[U, T_order, T] = feval([M_.fname '.objective.sparse.static_resid'], ys, zeros(1,exo_nbr), M_.params);
[Uy, T_order, T] = feval([M_.fname '.objective.sparse.static_g1'], ys, zeros(1,exo_nbr), M_.params, M_.objective_g1_sparse_rowval, M_.objective_g1_sparse_colval, M_.objective_g1_sparse_colptr, T_order, T);
Uyy_v = feval([M_.fname '.objective.sparse.static_g2'], ys, zeros(1,exo_nbr), M_.params, T_order, T);
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 = build_two_dim_hessian(M_.objective_g2_sparse_indices, Uyy_v, 1, M_.endo_nbr);
Uyy = full(Uyy);
Uyygygy = A_times_B_kronecker_C(Uyy,gy,gy);
@ -305,7 +312,7 @@ else
dr.(['g_' num2str(i)]) = [dr.(['g_' num2str(i)]); W.(['W_' num2str(i)])];
end
% Amends the steady-state vector accordingly
[U] = feval([M_.fname '.objective.static'],ys,zeros(1,exo_nbr), M_.params);
U = feval([M_.fname '.objective.sparse.static_resid'], ys, zeros(1,exo_nbr), M_.params);
ysteady = [ys(oo_.dr.order_var); U/(1-beta)];
% Generates the sequence of shocks to compute unconditional welfare