Remove unused routines.
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4e90a47521
commit
c492ce7b73
GPG Key ID:
295C1FE89E17EB3C
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function r = ep_residuals(x, y, ix, iy, steadystate, dr, maximum_lag, endo_nbr)
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% Inversion of the extended path simulation approach. This routine computes the innovations needed to
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% reproduce the time path of a subset of endogenous variables.
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%
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% INPUTS
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% o x [double] n*1 vector, time t innovations.
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% o y [double] n*1 vector, time t restricted endogenous variables.
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% o ix [integer] index of control innovations in the full vector of innovations.
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% o iy [integer] index of controlled variables in the full vector of endogenous variables.
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% o s [double] m*1 vector, endogenous variables at time t-1.
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%
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%
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% OUTPUTS
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% o r [double] n*1 vector of residuals.
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%
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% ALGORITHM
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%
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% SPECIAL REQUIREMENTS
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% Copyright © 2010-2017 Dynare Team.
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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global oo_ options_
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persistent k1 k2 weight
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if isempty(k1)
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k1 = [maximum_lag:-1:1];
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k2 = dr.kstate(find(dr.kstate(:,2) <= maximum_lag+1),[1 2]);
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k2 = k2(:,1)+(maximum_lag+1-k2(:,2))*endo_nbr;
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weight = 0.0;
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end
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verbose = options_.ep.verbosity;
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% Copy the shocks in exo_simul.
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oo_.exo_simul(maximum_lag+1,ix) = exp(transpose(x));
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exo_simul = log(oo_.exo_simul);
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% Compute the initial solution path for the endogenous variables using a first order approximation.
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if verbose
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disp('ep_residuals:: Set initial condition for endogenous variable paths.')
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end
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initial_path = oo_.endo_simul;
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for i = maximum_lag+1:size(oo_.exo_simul)
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tempx1 = oo_.endo_simul(dr.order_var,k1);
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tempx2 = bsxfun(@minus,tempx1,dr.ys(dr.order_var));
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tempx = tempx2(k2);
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initial_path(dr.order_var,i) = dr.ys(dr.order_var)+dr.ghx*tempx2(k2)+dr.ghu*transpose(exo_simul(i,:));
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k1 = k1+1;
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end
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oo_.endo_simul = weight*initial_path + (1-weight)*oo_.endo_simul;
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info = perfect_foresight_simulation(dr,steadystate);
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if verbose>1
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info
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info.iterations.errors
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end
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r = y-transpose(oo_.endo_simul(maximum_lag+1,iy));
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%(re)Set k1 (indices for the initial conditions)
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k1 = [maximum_lag:-1:1];
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@ -1,111 +0,0 @@
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function innovation_paths = reversed_extended_path(controlled_variable_names, control_innovation_names, dataset)
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% Inversion of the extended path simulation approach. This routine computes the innovations needed to
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% reproduce the time path of a subset of endogenous variables. The initial condition is teh deterministic
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% steady state.
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%
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% INPUTS
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% o controlled_variable_names [string] n*1 matlab's cell.
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% o control_innovation_names [string] n*1 matlab's cell.
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% o dataset [structure]
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% OUTPUTS
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% o innovations [double] n*T matrix.
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%
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% ALGORITHM
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%
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% SPECIAL REQUIREMENTS
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% Copyright © 2010-2022 Dynare Team.
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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global M_ oo_ options_
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%% Initialization
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% Load data.
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eval(dataset.name);
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dataset.data = [];
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for v = 1:dataset.number_of_observed_variables
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eval(['dataset.data = [ dataset.data , ' dataset.variables(v,:) ' ];'])
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end
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data = dataset.data(dataset.first_observation:dataset.first_observation+dataset.number_of_observations,:);
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% Compute the deterministic steady state.
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[oo_.steady_state, M_.params] = evaluate_steady_state(oo_.steady_state, M_, options_, oo_, ~options_.steadystate.nocheck);
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% Compute the first order perturbation reduced form.
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old_options_order = options_.order; options_.order = 1;
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[dr,info,M_,oo_] = compute_decision_rules(M_,options_,oo_);
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oo_.dr = dr;
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options_.order = old_options_order;
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% Set various options.
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options_.periods = 100;
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% Set-up oo_.exo_simul.
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oo_=make_ex_(M_,options_,oo_);
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% Set-up oo_.endo_simul.
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oo_=make_y_(M_,options_,oo_);
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% Get indices of the controlled endogenous variables in endo_simul.
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n = length(controlled_variable_names);
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iy = NaN(n,1);
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for k=1:n
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iy(k) = strmatch(controlled_variable_names{k}, M_.endo_names, 'exact');
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end
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% Get indices of the controlled endogenous variables in dataset.
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iy_ = NaN(n,1);
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for k=1:n
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iy_(k) = strmatch(controlled_variable_names{k},dataset.variables,'exact');
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end
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% Get indices of the control innovations in exo_simul.
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ix = NaN(n,1);
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for k=1:n
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ix(k) = strmatch(control_innovation_names{k},M_.exo_names,'exact');
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end
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% Get the length of the sample.
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T = size(data,1);
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% Output initialization.
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innovation_paths = zeros(n,T);
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% Initialization of the perfect foresight model solver.
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perfect_foresight_simulation();
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% Set options for fsolve.
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options = optimset('MaxIter',10000,'Display','Iter');
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%% Call fsolve recursively
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for t=1:T
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x0 = zeros(n,1);
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y_target = transpose(data(t,iy_));
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total_variation = y_target-transpose(oo_.endo_simul(t+M_.maximum_lag,iy));
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for i=1:100
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[t,i]
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y = transpose(oo_.endo_simul(t+M_.maximum_lag,iy)) + (i/100)*y_target
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[tmp,fval,exitflag] = fsolve('ep_residuals', x0, options, y, ix, iy, oo_.steady_state, oo_.dr, M_.maximum_lag, M_.endo_nbr);
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end
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if exitflag==1
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innovation_paths(:,t) = tmp;
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end
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% Update endo_simul.
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oo_.endo_simul(:,1:end-1) = oo_.endo_simul(:,2:end);
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oo_.endo_simul(:,end) = oo_.steady_state;
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end
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