92 lines
3.1 KiB
Matlab
92 lines
3.1 KiB
Matlab
function DynareOutput = simul_backward_linear_model(initial_conditions, sample_size, DynareOptions, DynareModel, DynareOutput, innovations)
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%@info:
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%! @deftypefn {Function File} {@var{DynareOutput} =} simul_backward_nonlinear_model (@var{sample_size},@var{DynareOptions}, @var{DynareModel}, @var{DynareOutput})
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%! @anchor{@simul_backward_nonlinear_model}
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%! @sp 1
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%! Simulates a stochastic non linear backward looking model with arbitrary precision (a deterministic solver is used).
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%! @sp 2
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%! @strong{Inputs}
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%! @sp 1
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%! @table @ @var
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%! @item sample_size
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%! Scalar integer, size of the sample to be generated.
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%! @item DynareOptions
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%! Matlab/Octave structure (Options used by Dynare).
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%! @item DynareDynareModel
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%! Matlab/Octave structure (Description of the model).
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%! @item DynareOutput
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%! Matlab/Octave structure (Results reported by Dynare).
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%! @end table
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%! @sp 1
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%! @strong{Outputs}
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%! @sp 1
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%! @table @ @var
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%! @item DynareOutput
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%! Matlab/Octave structure (Results reported by Dynare).
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%! @end table
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%! @sp 2
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%! @strong{This function is called by:}
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%! @sp 2
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%! @strong{This function calls:}
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%! @ref{dynTime}
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%!
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%! @end deftypefn
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%@eod:
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% Copyright (C) 2012-2016 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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number_of_shocks = size(DynareOutput.exo_simul,2);
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% Get usefull vector of indices.
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ny0 = nnz(DynareModel.lead_lag_incidence(2,:));
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ny1 = nnz(DynareModel.lead_lag_incidence(1,:));
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iy1 = find(DynareModel.lead_lag_incidence(1,:)>0);
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idx = 1:DynareModel.endo_nbr;
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jdx = idx+ny1;
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hdx = 1:ny1;
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% Get the name of the dynamic model routine.
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model_dynamic = str2func([DynareModel.fname,'_dynamic']);
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% initialization of vector y.
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y = NaN(length(idx)+ny1,1);
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% initialization of the returned simulations.
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DynareOutput.endo_simul = NaN(DynareModel.endo_nbr,sample_size+1);
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if isempty(initial_conditions)
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DynareOutput.endo_simul(:,1) = DynareOutput.steady_state;
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else
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DynareOutput.endo_simul(:,1) = initial_conditions;
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end
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Y = DynareOutput.endo_simul;
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% get coefficients
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[cst,jacob] = model_dynamic(zeros(DynareModel.endo_nbr+ny1,1), ...
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zeros(2,size(DynareOutput.exo_simul, 2)), ...
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DynareModel.params, ...
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DynareOutput.steadystate,2);
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A0inv = inv(jacob(:,jdx));
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A1 = jacob(:,nonzeros(DynareModel.lead_lag_incidence(1,:)));
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B = jacob(:,end-number_of_shocks+1:end);
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% Simulations
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for it = 2:sample_size+1
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Y(:,it) = -A0inv*(cst + A1*Y(iy1,it-1) + B*DynareOutput.exo_simul(it,:)');
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end
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DynareOutput.endo_simul = Y; |