77 lines
2.5 KiB
Matlab
77 lines
2.5 KiB
Matlab
function y = irf(M_, options_, dr, e1, long, drop, replic, iorder)
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% function y = irf(M_, options_, dr, e1, long, drop, replic, iorder)
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% Computes impulse response functions
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%
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% INPUTS
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% M_: structure representing the model
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% options_: structure representing options for commands
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% dr: structure of decisions rules for stochastic simulations
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% e1: exogenous variables value in time 1 after one shock
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% long: number of periods of simulation
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% drop: truncation (in order 2)
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% replic: number of replications (in order 2)
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% iorder: first or second order approximation
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%
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% OUTPUTS
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% y: impulse response matrix
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright © 2003-2019 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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if options_.loglinear && ~options_.logged_steady_state
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dr.ys = log_variable(1:M_.endo_nbr,dr.ys,M_);
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options_.logged_steady_state=1;
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end
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if M_.maximum_lag >= 1
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temps = repmat(dr.ys,1,M_.maximum_lag);
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else
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temps = zeros(M_.endo_nbr, 1); % Dummy values for purely forward models
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end
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y = 0;
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local_order = iorder;
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if local_order~=1 && M_.hessian_eq_zero
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local_order = 1;
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end
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if local_order == 1
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y1 = repmat(dr.ys,1,long);
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ex2 = zeros(long,M_.exo_nbr);
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ex2(1,:) = e1';
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y2 = simult_(M_,options_,temps,dr,ex2,local_order);
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y = y2(:,M_.maximum_lag+1:end)-y1;
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else
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% eliminate shocks with 0 variance
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i_exo_var = setdiff(1:M_.exo_nbr,find(diag(M_.Sigma_e) == 0 ));
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nxs = length(i_exo_var);
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ex1 = zeros(long+drop,M_.exo_nbr);
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chol_S = chol(M_.Sigma_e(i_exo_var,i_exo_var));
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for j = 1: replic
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ex1(:,i_exo_var) = randn(long+drop,nxs)*chol_S;
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ex2 = ex1;
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ex2(drop+1,:) = ex2(drop+1,:)+e1';
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y1 = simult_(M_,options_,temps,dr,ex1,local_order);
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y2 = simult_(M_,options_,temps,dr,ex2,local_order);
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y = y+(y2(:,M_.maximum_lag+drop+1:end)-y1(:,M_.maximum_lag+drop+1:end));
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end
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y=y/replic;
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end
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