139 lines
5.1 KiB
Matlab
139 lines
5.1 KiB
Matlab
function [initial_conditions, innovations, pfm, ep, verbosity, DynareOptions, DynareResults] = extended_path_initialization(initial_conditions, sample_size, exogenousvariables, DynareOptions, DynareModel, DynareResults)
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% Initialization of the extended path routines.
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%
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% INPUTS
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% o initial_conditions [double] m*1 array, where m is the number of endogenous variables in the model.
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% o sample_size [integer] scalar, size of the sample to be simulated.
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% o exogenousvariables [double] T*n array, values for the structural innovations.
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% o DynareOptions [struct] options_
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% o DynareModel [struct] M_
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% o DynareResults [struct] oo_
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%
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% OUTPUTS
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%
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% ALGORITHM
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%
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% SPECIAL REQUIREMENTS
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% Copyright © 2016-2020 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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ep = DynareOptions.ep;
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% Set verbosity levels.
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DynareOptions.verbosity = ep.verbosity;
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verbosity = ep.verbosity+ep.debug;
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% Set maximum number of iterations for the deterministic solver.
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DynareOptions.simul.maxit = ep.maxit;
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% Prepare a structure needed by the matlab implementation of the perfect foresight model solver
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pfm = setup_stochastic_perfect_foresight_model_solver(DynareModel, DynareOptions, DynareResults);
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% Check that the user did not use varexo_det
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if DynareModel.exo_det_nbr~=0
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error('Extended path does not support varexo_det.')
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end
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% Set default initial conditions.
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if isempty(initial_conditions)
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if isempty(DynareModel.endo_histval)
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initial_conditions = DynareResults.steady_state;
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else
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initial_conditions = DynareModel.endo_histval;
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end
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end
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% Set the number of periods for the (stochastic) perfect foresight model
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pfm.periods = ep.periods;
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pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
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pfm.block = DynareOptions.block;
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% Set the algorithm for the perfect foresight solver
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DynareOptions.stack_solve_algo = ep.stack_solve_algo;
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% Compute the first order reduced form if needed.
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%
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% REMARK. It is assumed that the user did run the same mod file with stoch_simul(order=1) and save
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% all the globals in a mat file called linear_reduced_form.mat;
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dr = struct();
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if ep.init
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DynareOptions.order = 1;
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DynareResults.dr=set_state_space(dr,DynareModel,DynareOptions);
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[DynareResults.dr,Info,DynareModel.params] = resol(0,DynareModel,DynareOptions,DynareResults.dr,DynareResults.steady_state, DynareResults.exo_steady_state, DynareResults.exo_det_steady_state);
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end
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% Do not use a minimal number of perdiods for the perfect foresight solver (with bytecode and blocks)
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DynareOptions.minimal_solving_period = DynareOptions.ep.periods;
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% Set the covariance matrix of the structural innovations.
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if isempty(exogenousvariables)
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innovations = struct();
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innovations.positive_var_indx = find(diag(DynareModel.Sigma_e)>0);
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innovations.effective_number_of_shocks = length(innovations.positive_var_indx);
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innovations.covariance_matrix = DynareModel.Sigma_e(innovations.positive_var_indx,innovations.positive_var_indx);
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innovations.covariance_matrix_upper_cholesky = chol(innovations.covariance_matrix);
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else
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innovations = struct();
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end
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% Set seed.
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if ep.set_dynare_seed_to_default
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DynareOptions=set_dynare_seed_local_options(DynareOptions,'default');
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end
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% hybrid correction
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pfm.hybrid_order = ep.stochastic.hybrid_order;
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if pfm.hybrid_order
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DynareResults.dr = set_state_space(DynareResults.dr, DynareModel, DynareOptions);
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options = DynareOptions;
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options.order = pfm.hybrid_order;
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[pfm.dr, DynareModel.params] = resol(0, DynareModel, options, DynareResults.dr, DynareResults.steady_state, DynareResults.exo_steady_state, DynareResults.exo_det_steady_state);
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else
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pfm.dr = [];
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end
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% number of nonzero derivatives
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pfm.nnzA = DynareModel.NNZDerivatives(1);
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% setting up integration nodes if order > 0
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if ep.stochastic.order > 0
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[nodes,weights,nnodes] = setup_integration_nodes(DynareOptions.ep,pfm);
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pfm.nodes = nodes;
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pfm.weights = weights;
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pfm.nnodes = nnodes;
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% compute number of blocks
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[block_nbr,pfm.world_nbr] = get_block_world_nbr(ep.stochastic.algo,nnodes,ep.stochastic.order,ep.periods);
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else
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block_nbr = ep.periods;
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end
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% set boundaries if mcp
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[lb,ub,pfm.eq_index] = get_complementarity_conditions(DynareModel, DynareOptions.ramsey_policy);
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if DynareOptions.ep.solve_algo == 10
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DynareOptions.lmmcp.lb = repmat(lb,block_nbr,1);
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DynareOptions.lmmcp.ub = repmat(ub,block_nbr,1);
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elseif DynareOptions.ep.solve_algo == 11
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DynareOptions.mcppath.lb = repmat(lb,block_nbr,1);
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DynareOptions.mcppath.ub = repmat(ub,block_nbr,1);
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end
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pfm.block_nbr = block_nbr;
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