Removed useless routine.
Stéphane Adjemian (Charybdis)
parent
e9c552c436
commit
63157949ca
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@ -1,405 +0,0 @@
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function time_series = extended_path_parfor(initial_conditions,sample_size)
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% Stochastic simulation of a non linear DSGE model using the Extended Path method (Fair and Taylor 1983). A time
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% series of size T is obtained by solving T perfect foresight models.
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%
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% INPUTS
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% o initial_conditions [double] m*nlags array, where m is the number of endogenous variables in the model and
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% nlags is the maximum number of lags.
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% o sample_size [integer] scalar, size of the sample to be simulated.
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%
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% OUTPUTS
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% o time_series [double] m*sample_size array, the simulations.
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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) 2009-2012 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 M_ options_ oo_
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options_.verbosity = options_.ep.verbosity;
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verbosity = options_.ep.verbosity+options_.ep.debug;
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% Prepare a structure needed by the matlab implementation of the perfect foresight model solver
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pfm.lead_lag_incidence = M_.lead_lag_incidence;
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pfm.ny = M_.endo_nbr;
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pfm.max_lag = M_.maximum_endo_lag;
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pfm.nyp = nnz(pfm.lead_lag_incidence(1,:));
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pfm.iyp = find(pfm.lead_lag_incidence(1,:)>0);
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pfm.ny0 = nnz(pfm.lead_lag_incidence(2,:));
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pfm.iy0 = find(pfm.lead_lag_incidence(2,:)>0);
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pfm.nyf = nnz(pfm.lead_lag_incidence(3,:));
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pfm.iyf = find(pfm.lead_lag_incidence(3,:)>0);
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pfm.nd = pfm.nyp+pfm.ny0+pfm.nyf;
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pfm.nrc = pfm.nyf+1;
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pfm.isp = [1:pfm.nyp];
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pfm.is = [pfm.nyp+1:pfm.ny+pfm.nyp];
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pfm.isf = pfm.iyf+pfm.nyp;
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pfm.isf1 = [pfm.nyp+pfm.ny+1:pfm.nyf+pfm.nyp+pfm.ny+1];
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pfm.iz = [1:pfm.ny+pfm.nyp+pfm.nyf];
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pfm.periods = options_.ep.periods;
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pfm.steady_state = oo_.steady_state;
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pfm.params = M_.params;
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pfm.i_cols_1 = nonzeros(pfm.lead_lag_incidence(2:3,:)');
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pfm.i_cols_A1 = find(pfm.lead_lag_incidence(2:3,:)');
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pfm.i_cols_T = nonzeros(pfm.lead_lag_incidence(1:2,:)');
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pfm.i_cols_j = 1:pfm.nd;
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pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
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pfm.dynamic_model = str2func([M_.fname,'_dynamic']);
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pfm.verbose = options_.ep.verbosity;
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pfm.maxit_ = options_.maxit_;
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pfm.tolerance = options_.dynatol.f;
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exo_nbr = M_.exo_nbr;
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periods = options_.periods;
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ep = options_.ep;
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steady_state = oo_.steady_state;
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dynatol = options_.dynatol;
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% Set default initial conditions.
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if isempty(initial_conditions)
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initial_conditions = oo_.steady_state;
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end
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% Set maximum number of iterations for the deterministic solver.
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options_.maxit_ = options_.ep.maxit;
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% Set the number of periods for the perfect foresight model
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periods = options_.ep.periods;
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pfm.periods = options_.ep.periods;
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pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
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% Set the algorithm for the perfect foresight solver
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options_.stack_solve_algo = options_.ep.stack_solve_algo;
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% Set check_stability flag
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do_not_check_stability_flag = ~options_.ep.check_stability;
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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 options_.ep.init
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options_.order = 1;
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[dr,Info,M_,options_,oo_] = resol(1,M_,options_,oo_);
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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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options_.minimal_solving_period = 100;%options_.ep.periods;
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% Initialize the exogenous variables.
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make_ex_;
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% Initialize the endogenous variables.
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make_y_;
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% Initialize the output array.
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time_series = zeros(M_.endo_nbr,sample_size);
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% Set the covariance matrix of the structural innovations.
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variances = diag(M_.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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stdd = sqrt(variances(positive_var_indx));
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covariance_matrix = M_.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.
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if options_.ep.set_dynare_seed_to_default
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set_dynare_seed('default');
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end
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% Set bytecode flag
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bytecode_flag = options_.ep.use_bytecode;
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% Simulate shocks.
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switch options_.ep.innovation_distribution
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case 'gaussian'
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oo_.ep.shocks = randn(sample_size,effective_number_of_shocks)*covariance_matrix_upper_cholesky;
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otherwise
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error(['extended_path:: ' options_.ep.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!'])
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end
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% Set future shocks (Stochastic Extended Path approach)
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if options_.ep.stochastic.status
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switch options_.ep.stochastic.method
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case 'tensor'
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switch options_.ep.stochastic.ortpol
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case 'hermite'
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[r,w] = gauss_hermite_weights_and_nodes(options_.ep.stochastic.nodes);
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otherwise
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error('extended_path:: Unknown orthogonal polynomial option!')
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end
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if options_.ep.stochastic.order*M_.exo_nbr>1
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for i=1:options_.ep.stochastic.order*M_.exo_nbr
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rr(i) = {r};
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ww(i) = {w};
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end
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rrr = cartesian_product_of_sets(rr{:});
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www = cartesian_product_of_sets(ww{:});
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else
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rrr = r;
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www = w;
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end
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www = prod(www,2);
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number_of_nodes = length(www);
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relative_weights = www/max(www);
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switch options_.ep.stochastic.pruned.status
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case 1
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jdx = find(relative_weights>options_.ep.stochastic.pruned.relative);
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www = www(jdx);
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www = www/sum(www);
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rrr = rrr(jdx,:);
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case 2
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jdx = find(weights>options_.ep.stochastic.pruned.level);
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www = www(jdx);
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www = www/sum(www);
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rrr = rrr(jdx,:);
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otherwise
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% Nothing to be done!
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end
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nnn = length(www);
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otherwise
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error('extended_path:: Unknown stochastic_method option!')
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end
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else
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rrr = zeros(1,effective_number_of_shocks);
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www = 1;
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nnn = 1;
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end
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% Initializes some variables.
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t = 0;
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% Set waitbar (graphic or text mode)
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hh = dyn_waitbar(0,'Please wait. Extended Path simulations...');
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set(hh,'Name','EP simulations.');
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if options_.ep.memory
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mArray1 = zeros(M_.endo_nbr,100,nnn,sample_size);
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mArray2 = zeros(M_.exo_nbr,100,nnn,sample_size);
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end
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% Main loop.
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while (t<sample_size)
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if ~mod(t,10)
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dyn_waitbar(t/sample_size,hh,'Please wait. Extended Path simulations...');
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end
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% Set period index.
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t = t+1;
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shocks = oo_.ep.shocks(t,:);
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% Put it in oo_.exo_simul (second line).
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oo_.exo_simul(2,positive_var_indx) = shocks;
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parfor s = 1:nnn
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periods1 = periods;
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exo_simul_1 = zeros(periods1+2,exo_nbr);
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pfm1 = pfm;
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info_convergence = 0;
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switch ep.stochastic.ortpol
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case 'hermite'
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for u=1:ep.stochastic.order
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exo_simul_1(2+u,positive_var_indx) = rrr(s,(((u-1)*effective_number_of_shocks)+1):(u*effective_number_of_shocks))*covariance_matrix_upper_cholesky;
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end
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otherwise
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error('extended_path:: Unknown orthogonal polynomial option!')
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end
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if ep.stochastic.order && ep.stochastic.scramble
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exo_simul_1(2+ep.stochastic.order+1:2+ep.stochastic.order+ep.stochastic.scramble,positive_var_indx) = ...
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randn(ep.stochastic.scramble,effective_number_of_shocks)*covariance_matrix_upper_cholesky;
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end
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if ep.init% Compute first order solution (Perturbation)...
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ex = zeros(size(endo_simul_1,2),size(exo_simul_1,2));
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ex(1:size(exo_simul_1,1),:) = exo_simul_1;
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exo_simul_1 = ex;
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initial_path = simult_(initial_conditions,dr,exo_simul_1(2:end,:),1);
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endo_simul_1(:,1:end-1) = initial_path(:,1:end-1)*ep.init+endo_simul_1(:,1:end-1)*(1-ep.init);
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else
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endo_simul_1 = repmat(steady_state,1,periods1+2);
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end
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% Solve a perfect foresight model.
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increase_periods = 0;
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endo_simul = endo_simul_1;
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while 1
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if ~increase_periods
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if bytecode_flag
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[flag,tmp] = bytecode('dynamic');
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else
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flag = 1;
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end
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if flag
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[flag,tmp] = solve_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm1);
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end
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info_convergence = ~flag;
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end
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if verbosity
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if info_convergence
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if t<10
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disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
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elseif t<100
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disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
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elseif t<1000
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disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
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else
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disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
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end
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else
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if t<10
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disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
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elseif t<100
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disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
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elseif t<1000
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disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
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else
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disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
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end
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end
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end
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if do_not_check_stability_flag
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% Exit from the while loop.
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endo_simul_1 = tmp;
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break
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else
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% Test if periods is big enough.
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% Increase the number of periods.
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periods1 = periods1 + ep.step;
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pfm1.periods = periods1;
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pfm1.i_upd = pfm1.ny+(1:pfm1.periods*pfm1.ny);
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% Increment the counter.
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increase_periods = increase_periods + 1;
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if verbosity
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if t<10
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disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(periods1) '.'])
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elseif t<100
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disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(periods1) '.'])
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elseif t<1000
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disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(periods1) '.'])
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else
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disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(periods1) '.'])
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end
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end
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if info_convergence
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% If the previous call to the perfect foresight model solver exited
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% announcing that the routine converged, adapt the size of endo_simul_1
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% and exo_simul_1.
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endo_simul_1 = [ tmp , repmat(steady_state,1,ep.step) ];
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exo_simul_1 = [ exo_simul_1 ; zeros(ep.step,size(shocks,2)) ];
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tmp_old = tmp;
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else
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% If the previous call to the perfect foresight model solver exited
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% announcing that the routine did not converge, then tmp=1... Maybe
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% should change that, because in some circonstances it may usefull
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% to know where the routine did stop, even if convergence was not
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% achieved.
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endo_simul_1 = [ endo_simul_1 , repmat(steady_state,1,ep.step) ];
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exo_simul_1 = [ exo_simul_1 ; zeros(ep.step,size(shocks,2)) ];
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end
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% Solve the perfect foresight model with an increased number of periods.
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if bytecode_flag
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[flag,tmp] = bytecode('dynamic');
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else
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flag = 1;
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end
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if flag
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[flag,tmp] = solve_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm1);
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end
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info_convergence = ~flag;
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if info_convergence
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% If the solver achieved convergence, check that simulated paths did not
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% change during the first periods.
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% Compute the maximum deviation between old path and new path over the
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% first periods
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delta = max(max(abs(tmp(:,2:ep.fp)-tmp_old(:,2:ep.fp))));
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if delta < dynatol.x
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% If the maximum deviation is close enough to zero, reset the number
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% of periods to ep.periods
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periods1 = ep.periods;
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pfm1.periods = periods1;
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pfm1.i_upd = pfm1.ny+(1:pfm1.periods*pfm1.ny);
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% Cut exo_simul_1 and endo_simul_1 consistently with the resetted
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% number of periods and exit from the while loop.
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exo_simul_1 = exo_simul_1(1:(periods1+2),:);
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endo_simul_1 = endo_simul_1(:,1:(periods1+2));
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break
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end
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else
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% The solver did not converge... Try to solve the model again with a bigger
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% number of periods, except if the number of periods has been increased more
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% than 10 times.
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if increase_periods==10;
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if verbosity
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if t<10
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disp(['Time: ' int2str(t) '. Even with ' int2str(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
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elseif t<100
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disp(['Time: ' int2str(t) '. Even with ' int2str(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
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elseif t<1000
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disp(['Time: ' int2str(t) '. Even with ' int2str(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
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else
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disp(['Time: ' int2str(t) '. Even with ' int2str(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
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end
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end
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% Exit from the while loop.
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break
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end
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end% if info_convergence
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end
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end% while
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if ~info_convergence% If exited from the while loop without achieving convergence, use an homotopic approach
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[INFO,tmp] = homotopic_steps(.5,.01,pfm1);
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if (~isstruct(INFO) && isnan(INFO)) || ~info_convergence
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[INFO,tmp] = homotopic_steps(0,.01,pfm1);
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if ~info_convergence
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disp('Homotopy:: No convergence of the perfect foresight model solver!')
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error('I am not able to simulate this model!');
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else
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info_convergence = 1;
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endo_simul_1 = tmp;
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if verbosity && info_convergence
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disp('Homotopy:: Convergence of the perfect foresight model solver!')
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end
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end
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else
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info_convergence = 1;
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endo_simul_1 = tmp;
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if verbosity && info_convergence
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disp('Homotopy:: Convergence of the perfect foresight model solver!')
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end
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end
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end
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% Save results of the perfect foresight model solver.
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if ep.memory
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mArray1(:,:,s,t) = endo_simul_1(:,1:100);
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mArrat2(:,:,s,t) = transpose(exo_simul_1(1:100,:));
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end
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results(:,s) = www(s)*endo_simul_1(:,2);
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end
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time_series(:,t) = sum(results,2);
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oo_.endo_simul(:,1:end-1) = oo_.endo_simul(:,2:end);
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oo_.endo_simul(:,1) = time_series(:,t);
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oo_.endo_simul(:,end) = oo_.steady_state;
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end% (while) loop over t
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dyn_waitbar_close(hh);
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oo_.endo_simul = oo_.steady_state;
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if ep.memory
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save([M_.fname '_memory'],'mArray1','mArray2','www');
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end
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