111 lines
3.5 KiB
Matlab
111 lines
3.5 KiB
Matlab
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 (C) 2010-2018 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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steady_;
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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_,options_,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 |