Code factorization.
Stéphane Adjemian (Scylla)
parent
ccfd809dd0
commit
77b1d14083
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@ -1,38 +1,25 @@
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function DynareOutput = simul_backward_linear_model(initial_conditions, sample_size, DynareOptions, DynareModel, DynareOutput, innovations)
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function DynareOutput = simul_backward_linear_model(varargin)
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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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% Simulates a stochastic linear backward looking model.
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%
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% INPUTS
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% - initialconditions [double] n*1 vector, initial conditions for the endogenous variables.
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% - samplesize [integer] scalar, number of periods for the simulation.
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% - DynareOptions [struct] Dynare's options_ global structure.
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% - DynareModel [struct] Dynare's M_ global structure.
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% - DynareOutput [struct] Dynare's oo_ global structure.
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% - innovations [double] T*q matrix, innovations to be used for the simulation.
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%
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% OUTPUTS
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% - DynareOutput [struct] Dynare's oo_ global structure.
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%
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% REMARKS
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% [1] The innovations used for the simulation are saved in DynareOutput.exo_simul, and the resulting paths for the endogenous
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% variables are saved in DynareOutput.endo_simul.
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% [2] The last input argument is not mandatory. If absent we use random draws and rescale them with the informations provided
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% through the shocks block.
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% [3] If the first input argument is empty, the endogenous variables are initialized with 0, or if available with the informations
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% provided thrtough the histval block.
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% Copyright (C) 2012-2017 Dynare Team
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%
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@ -51,27 +38,15 @@ function DynareOutput = simul_backward_linear_model(initial_conditions, sample_s
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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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[initialconditions, samplesize, innovations, DynareOptions, DynareModel, DynareOutput, nx, ny1, iy1, jdx, model_dynamic, y] = ...
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simul_backward_model_init(varargin{:});
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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 = NaN(DynareModel.endo_nbr,samplesize+1);
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if isempty(initialconditions)
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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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DynareOutput.endo_simul(:,1) = initialconditions;
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end
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Y = DynareOutput.endo_simul;
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@ -82,10 +57,10 @@ Y = DynareOutput.endo_simul;
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DynareOutput.steady_state,1);
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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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B = jacob(:,end-nx+1:end);
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% Simulations
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for it = 2:sample_size+1
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for it = 2:samplesize+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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@ -1,38 +1,25 @@
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function DynareOutput = simul_backward_model(initial_conditions, sample_size, DynareOptions, DynareModel, DynareOutput, innovations)
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function DynareOutput = simul_backward_model(varargin)
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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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% Simulates a stochastic backward looking model (with arbitrary precision).
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%
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% INPUTS
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% - initialconditions [double] n*1 vector, initial conditions for the endogenous variables.
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% - samplesize [integer] scalar, number of periods for the simulation.
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% - DynareOptions [struct] Dynare's options_ global structure.
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% - DynareModel [struct] Dynare's M_ global structure.
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% - DynareOutput [struct] Dynare's oo_ global structure.
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% - innovations [double] T*q matrix, innovations to be used for the simulation.
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%
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% OUTPUTS
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% - DynareOutput [struct] Dynare's oo_ global structure.
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%
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% REMARKS
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% [1] The innovations used for the simulation are saved in DynareOutput.exo_simul, and the resulting paths for the endogenous
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% variables are saved in DynareOutput.endo_simul.
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% [2] The last input argument is not mandatory. If absent we use random draws and rescale them with the informations provided
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% through the shocks block.
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% [3] If the first input argument is empty, the endogenous variables are initialized with 0, or if available with the informations
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% provided thrtough the histval block.
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% Copyright (C) 2012-2017 Dynare Team
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%
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@ -51,48 +38,10 @@ function DynareOutput = simul_backward_model(initial_conditions, sample_size, Dy
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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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if DynareModel.maximum_lead
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error(['simul_backward_nonlinear_model:: The specified model is not backward looking!'])
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end
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if nargin<6
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% Set the covariance matrix of the structural innovations.
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variances = diag(DynareModel.Sigma_e);
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number_of_shocks = length(DynareModel.Sigma_e);
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positive_var_indx = find(variances>0);
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effective_number_of_shocks = length(positive_var_indx);
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covariance_matrix = DynareModel.Sigma_e(positive_var_indx,positive_var_indx);
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covariance_matrix_upper_cholesky = chol(covariance_matrix);
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% Set seed to its default state.
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if DynareOptions.bnlms.set_dynare_seed_to_default
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set_dynare_seed('default');
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end
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% Simulate structural innovations.
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switch DynareOptions.bnlms.innovation_distribution
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case 'gaussian'
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DynareOutput.bnlms.shocks = randn(sample_size,effective_number_of_shocks)*covariance_matrix_upper_cholesky;
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otherwise
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error(['simul_backward_nonlinear_model:: ' DynareOption.bnlms.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!'])
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end
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% Put the simulated innovations in DynareOutput.exo_simul.
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DynareOutput.exo_simul = zeros(sample_size, number_of_shocks);
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DynareOutput.exo_simul(2:end,positive_var_indx) = DynareOutput.bnlms.shocks;
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if isfield(DynareModel,'exo_histval') && ~isempty(DynareModel.exo_histval)
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DynareOutput.exo_simul = [M_.exo_histval; DynareOutput.exo_simul];
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else
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DynareOutput.exo_simul = [zeros(1,number_of_shocks); DynareOutput.exo_simul];
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end
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else
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number_of_shocks = size(innovations,2);
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DynareOutput.exo_simul = innovations;
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end
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[initialconditions, samplesize, innovations, DynareOptions, DynareModel, DynareOutput] = simul_backward_model_init(varargin{:});
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if DynareOptions.linear
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DynareOutput = simul_backward_linear_model(initial_conditions, sample_size, DynareOptions, ...
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DynareModel, DynareOutput, innovations);
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DynareOutput = simul_backward_linear_model(initialconditions, samplesize, DynareOptions, DynareModel, DynareOutput, innovations);
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else
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DynareOutput = simul_backward_nonlinear_model(initial_conditions, sample_size, DynareOptions, ...
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DynareModel, DynareOutput, innovations);
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DynareOutput = simul_backward_nonlinear_model(initialconditions, samplesize, DynareOptions, DynareModel, DynareOutput, innovations);
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end
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@ -0,0 +1,77 @@
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function [initialconditions, samplesize, innovations, DynareOptions, DynareModel, DynareOutput, nx, ny1, iy1, jdx, model_dynamic, y] = simul_backward_model_init(varargin)
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% Initialization of the routines simulating backward models.
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% Copyright (C) 2012-2017 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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initialconditions = varargin{1};
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samplesize = varargin{2};
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DynareOptions = varargin{3};
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DynareModel = varargin{4};
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DynareOutput = varargin{5};
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if DynareModel.maximum_lead
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error('simul_backward_nonlinear_model:: The specified model is not backward looking!')
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end
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if nargin<6
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% Set the covariance matrix of the structural innovations.
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variances = diag(DynareModel.Sigma_e);
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number_of_shocks = length(DynareModel.Sigma_e);
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positive_var_indx = find(variances>0);
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effective_number_of_shocks = length(positive_var_indx);
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covariance_matrix = DynareModel.Sigma_e(positive_var_indx,positive_var_indx);
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covariance_matrix_upper_cholesky = chol(covariance_matrix);
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% Set seed to its default state.
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if DynareOptions.bnlms.set_dynare_seed_to_default
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set_dynare_seed('default');
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end
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% Simulate structural innovations.
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switch DynareOptions.bnlms.innovation_distribution
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case 'gaussian'
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DynareOutput.bnlms.shocks = randn(samplesize,effective_number_of_shocks)*covariance_matrix_upper_cholesky;
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otherwise
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error(['simul_backward_nonlinear_model:: ' DynareOption.bnlms.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!'])
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end
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% Put the simulated innovations in DynareOutput.exo_simul.
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DynareOutput.exo_simul = zeros(samplesize,number_of_shocks);
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DynareOutput.exo_simul(:,positive_var_indx) = DynareOutput.bnlms.shocks;
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if isfield(DynareModel,'exo_histval') && ~ isempty(DynareModel.exo_histval)
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DynareOutput.exo_simul = [transpose(DynareModel.exo_histval); DynareOutput.exo_simul];
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else
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DynareOutput.exo_simul = [zeros(1,number_of_shocks); DynareOutput.exo_simul];
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end
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innovations = DynareOutput.exo_simul;
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else
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innovations = varargin{6};
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DynareOutput.exo_simul = innovations; % innovations
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end
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if nargout>6
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nx = size(DynareOutput.exo_simul,2);
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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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% 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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end
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@ -1,4 +1,4 @@
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function DynareOutput = simul_backward_nonlinear_model(initial_conditions, sample_size, DynareOptions, DynareModel, DynareOutput, innovations)
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function DynareOutput = simul_backward_nonlinear_model(varargin)
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% Simulates a stochastic non linear backward looking model with arbitrary precision (a deterministic solver is used).
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%
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@ -38,58 +38,14 @@ function DynareOutput = simul_backward_nonlinear_model(initial_conditions, sampl
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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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if DynareModel.maximum_lead
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error('simul_backward_nonlinear_model:: The specified model is not backward looking!')
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end
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[initialconditions, samplesize, innovations, DynareOptions, DynareModel, DynareOutput, nx, ny1, iy1, jdx, model_dynamic, y] = ...
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simul_backward_model_init(varargin{:});
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if nargin<6
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% Set the covariance matrix of the structural innovations.
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variances = diag(DynareModel.Sigma_e);
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number_of_shocks = length(DynareModel.Sigma_e);
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positive_var_indx = find(variances>0);
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effective_number_of_shocks = length(positive_var_indx);
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covariance_matrix = DynareModel.Sigma_e(positive_var_indx,positive_var_indx);
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covariance_matrix_upper_cholesky = chol(covariance_matrix);
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% Set seed to its default state.
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if DynareOptions.bnlms.set_dynare_seed_to_default
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set_dynare_seed('default');
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end
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% Simulate structural innovations.
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switch DynareOptions.bnlms.innovation_distribution
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case 'gaussian'
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DynareOutput.bnlms.shocks = randn(sample_size,effective_number_of_shocks)*covariance_matrix_upper_cholesky;
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otherwise
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error(['simul_backward_nonlinear_model:: ' DynareOption.bnlms.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!'])
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end
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% Put the simulated innovations in DynareOutput.exo_simul.
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DynareOutput.exo_simul = zeros(sample_size,number_of_shocks);
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DynareOutput.exo_simul(:,positive_var_indx) = DynareOutput.bnlms.shocks;
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if isfield(DynareModel,'exo_histval') && ~ isempty(DynareModel.exo_histval)
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DynareOutput.exo_simul = [transpose(DynareModel.exo_histval); DynareOutput.exo_simul];
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else
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DynareOutput.exo_simul = [zeros(1,number_of_shocks); DynareOutput.exo_simul];
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end
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else
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DynareOutput.exo_simul = innovations;
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end
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% Get usefull vector of indices.
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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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model_dynamic_s = str2func('dynamic_backward_model_for_simulation');
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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 = NaN(DynareModel.endo_nbr,samplesize+1);
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if isempty(initialconditions)
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if isfield(DynareModel,'endo_histval') && ~isempty(DynareModel.endo_histval)
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DynareOutput.endo_simul(:,1:DynareModel.maximum_lag) = DynareModel.endo_histval;
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else
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@ -97,13 +53,13 @@ if isempty(initial_conditions)
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DynareOutput.endo_simul(:,1) = 0;
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end
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else
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DynareOutput.endo_simul(:,1) = initial_conditions;
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DynareOutput.endo_simul(:,1) = initialconditions;
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end
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Y = DynareOutput.endo_simul;
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% Simulations (call a Newton-like algorithm for each period).
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for it = 2:sample_size+1
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for it = 2:samplesize+1
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ylag = Y(iy1,it-1); % Set lagged variables.
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y = Y(:,it-1); % A good guess for the initial conditions is the previous values for the endogenous variables.
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Y(:,it) = dynare_solve(model_dynamic_s, y, DynareOptions, model_dynamic, ylag, DynareOutput.exo_simul, DynareModel.params, DynareOutput.steady_state, it+(DynareModel.maximum_exo_lag-1));
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Reference in New Issue