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evaluate_planner_objective.m: compute required moments directly

trust-region-mex
Johannes Pfeifer 2021-07-22 15:01:15 +02:00
parent dc45ac6361
commit 11c75592fb
1 changed files with 21 additions and 14 deletions
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35
matlab/evaluate_planner_objective.m
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@ -1,13 +1,22 @@
function planner_objective_value = evaluate_planner_objective(M_,options_,oo_)
% function planner_objective_value = evaluate_planner_objective(M_,options_,oo_)
% INPUTS
% M_: (structure) model description
% options_: (structure) options
% oo_: (structure) output results
% OUTPUT
% Returns a vector containing first order or second-order approximations of
% planner_objective_value (double)
%
%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.
%
% SPECIAL REQUIREMENTS
% none
% ALGORITHM
% Welfare verifies
% Welfare satifies
% W(y_{t-1}, u_t, sigma) = U(h(y_{t-1}, u_t, sigma)) + beta E_t W(g(y_{t-1}, u_t, sigma), u_t, sigma)
% where
% - W is the welfare function
@ -20,7 +29,7 @@ function planner_objective_value = evaluate_planner_objective(M_,options_,oo_)
% - beta is the planner's discount factor
% - E_t is the expectation operator given information at time t i.e. (y_{t-1}, u_t, sigma)
% The unconditional expectation of the planner's objective function verifies
% The unconditional expectation of the planner's objective function satisfies
% E(W) = E(U)/(1-beta)
% The conditional expectation of the planner's objective function given (y_{t-1}, u_t, sigma) coincides with the welfare function delineated above.
@ -51,14 +60,6 @@ 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.
% INPUTS
% M_: (structure) model description
% options_: (structure) options
% oo_: (structure) output results
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2007-2021 Dynare Team
%
% This file is part of Dynare.
@ -77,6 +78,9 @@ function planner_objective_value = evaluate_planner_objective(M_,options_,oo_)
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
dr = oo_.dr;
if isempty(options_.qz_criterium)
options_.qz_criterium = 1+1e-6;
end
exo_nbr = M_.exo_nbr;
nstatic = M_.nstatic;
@ -132,7 +136,10 @@ if options_.ramsey_policy
options_.noprint = 1;
end
var_list = M_.endo_names(dr.order_var(nstatic+(1:nspred)));
[info, oo_, options_] = stoch_simul(M_, options_, oo_, var_list); %get decision rules and moments
if options_.pruning
fprintf('evaluate_planner_objective: pruning option is not supported and will be ignored\n')
end
oo_=disp_th_moments(dr,var_list,M_,options_,oo_);
if ~old_noprint
options_.noprint = 0;
end
@ -198,7 +205,7 @@ elseif options_.discretionary_policy
options_.noprint = 1;
end
var_list = M_.endo_names(dr.order_var(nstatic+(1:nspred)));
[info, oo_, options_] = stoch_simul(M_, options_, oo_, var_list); %get decision rules and moments
oo_=disp_th_moments(dr,var_list,M_,options_,oo_);
if ~old_noprint
options_.noprint = 0;
end