77 lines
2.6 KiB
Matlab
77 lines
2.6 KiB
Matlab
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 (C) 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 <http://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]; |