Commit 63157949 authored by Stéphane Adjemian's avatar Stéphane Adjemian
Browse files

Removed useless routine.

parent e9c552c4
function time_series = extended_path_parfor(initial_conditions,sample_size)
% Stochastic simulation of a non linear DSGE model using the Extended Path method (Fair and Taylor 1983). A time
% series of size T is obtained by solving T perfect foresight models.
%
% INPUTS
% o initial_conditions [double] m*nlags array, where m is the number of endogenous variables in the model and
% nlags is the maximum number of lags.
% o sample_size [integer] scalar, size of the sample to be simulated.
%
% OUTPUTS
% o time_series [double] m*sample_size array, the simulations.
%
% ALGORITHM
%
% SPECIAL REQUIREMENTS
% Copyright (C) 2009-2012 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global M_ options_ oo_
options_.verbosity = options_.ep.verbosity;
verbosity = options_.ep.verbosity+options_.ep.debug;
% Prepare a structure needed by the matlab implementation of the perfect foresight model solver
pfm.lead_lag_incidence = M_.lead_lag_incidence;
pfm.ny = M_.endo_nbr;
pfm.max_lag = M_.maximum_endo_lag;
pfm.nyp = nnz(pfm.lead_lag_incidence(1,:));
pfm.iyp = find(pfm.lead_lag_incidence(1,:)>0);
pfm.ny0 = nnz(pfm.lead_lag_incidence(2,:));
pfm.iy0 = find(pfm.lead_lag_incidence(2,:)>0);
pfm.nyf = nnz(pfm.lead_lag_incidence(3,:));
pfm.iyf = find(pfm.lead_lag_incidence(3,:)>0);
pfm.nd = pfm.nyp+pfm.ny0+pfm.nyf;
pfm.nrc = pfm.nyf+1;
pfm.isp = [1:pfm.nyp];
pfm.is = [pfm.nyp+1:pfm.ny+pfm.nyp];
pfm.isf = pfm.iyf+pfm.nyp;
pfm.isf1 = [pfm.nyp+pfm.ny+1:pfm.nyf+pfm.nyp+pfm.ny+1];
pfm.iz = [1:pfm.ny+pfm.nyp+pfm.nyf];
pfm.periods = options_.ep.periods;
pfm.steady_state = oo_.steady_state;
pfm.params = M_.params;
pfm.i_cols_1 = nonzeros(pfm.lead_lag_incidence(2:3,:)');
pfm.i_cols_A1 = find(pfm.lead_lag_incidence(2:3,:)');
pfm.i_cols_T = nonzeros(pfm.lead_lag_incidence(1:2,:)');
pfm.i_cols_j = 1:pfm.nd;
pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
pfm.dynamic_model = str2func([M_.fname,'_dynamic']);
pfm.verbose = options_.ep.verbosity;
pfm.maxit_ = options_.maxit_;
pfm.tolerance = options_.dynatol.f;
exo_nbr = M_.exo_nbr;
periods = options_.periods;
ep = options_.ep;
steady_state = oo_.steady_state;
dynatol = options_.dynatol;
% Set default initial conditions.
if isempty(initial_conditions)
initial_conditions = oo_.steady_state;
end
% Set maximum number of iterations for the deterministic solver.
options_.maxit_ = options_.ep.maxit;
% Set the number of periods for the perfect foresight model
periods = options_.ep.periods;
pfm.periods = options_.ep.periods;
pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
% Set the algorithm for the perfect foresight solver
options_.stack_solve_algo = options_.ep.stack_solve_algo;
% Set check_stability flag
do_not_check_stability_flag = ~options_.ep.check_stability;
% Compute the first order reduced form if needed.
%
% REMARK. It is assumed that the user did run the same mod file with stoch_simul(order=1) and save
% all the globals in a mat file called linear_reduced_form.mat;
dr = struct();
if options_.ep.init
options_.order = 1;
[dr,Info,M_,options_,oo_] = resol(1,M_,options_,oo_);
end
% Do not use a minimal number of perdiods for the perfect foresight solver (with bytecode and blocks)
options_.minimal_solving_period = 100;%options_.ep.periods;
% Initialize the exogenous variables.
make_ex_;
% Initialize the endogenous variables.
make_y_;
% Initialize the output array.
time_series = zeros(M_.endo_nbr,sample_size);
% Set the covariance matrix of the structural innovations.
variances = diag(M_.Sigma_e);
positive_var_indx = find(variances>0);
effective_number_of_shocks = length(positive_var_indx);
stdd = sqrt(variances(positive_var_indx));
covariance_matrix = M_.Sigma_e(positive_var_indx,positive_var_indx);
covariance_matrix_upper_cholesky = chol(covariance_matrix);
% Set seed.
if options_.ep.set_dynare_seed_to_default
set_dynare_seed('default');
end
% Set bytecode flag
bytecode_flag = options_.ep.use_bytecode;
% Simulate shocks.
switch options_.ep.innovation_distribution
case 'gaussian'
oo_.ep.shocks = randn(sample_size,effective_number_of_shocks)*covariance_matrix_upper_cholesky;
otherwise
error(['extended_path:: ' options_.ep.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!'])
end
% Set future shocks (Stochastic Extended Path approach)
if options_.ep.stochastic.status
switch options_.ep.stochastic.method
case 'tensor'
switch options_.ep.stochastic.ortpol
case 'hermite'
[r,w] = gauss_hermite_weights_and_nodes(options_.ep.stochastic.nodes);
otherwise
error('extended_path:: Unknown orthogonal polynomial option!')
end
if options_.ep.stochastic.order*M_.exo_nbr>1
for i=1:options_.ep.stochastic.order*M_.exo_nbr
rr(i) = {r};
ww(i) = {w};
end
rrr = cartesian_product_of_sets(rr{:});
www = cartesian_product_of_sets(ww{:});
else
rrr = r;
www = w;
end
www = prod(www,2);
number_of_nodes = length(www);
relative_weights = www/max(www);
switch options_.ep.stochastic.pruned.status
case 1
jdx = find(relative_weights>options_.ep.stochastic.pruned.relative);
www = www(jdx);
www = www/sum(www);
rrr = rrr(jdx,:);
case 2
jdx = find(weights>options_.ep.stochastic.pruned.level);
www = www(jdx);
www = www/sum(www);
rrr = rrr(jdx,:);
otherwise
% Nothing to be done!
end
nnn = length(www);
otherwise
error('extended_path:: Unknown stochastic_method option!')
end
else
rrr = zeros(1,effective_number_of_shocks);
www = 1;
nnn = 1;
end
% Initializes some variables.
t = 0;
% Set waitbar (graphic or text mode)
hh = dyn_waitbar(0,'Please wait. Extended Path simulations...');
set(hh,'Name','EP simulations.');
if options_.ep.memory
mArray1 = zeros(M_.endo_nbr,100,nnn,sample_size);
mArray2 = zeros(M_.exo_nbr,100,nnn,sample_size);
end
% Main loop.
while (t<sample_size)
if ~mod(t,10)
dyn_waitbar(t/sample_size,hh,'Please wait. Extended Path simulations...');
end
% Set period index.
t = t+1;
shocks = oo_.ep.shocks(t,:);
% Put it in oo_.exo_simul (second line).
oo_.exo_simul(2,positive_var_indx) = shocks;
parfor s = 1:nnn
periods1 = periods;
exo_simul_1 = zeros(periods1+2,exo_nbr);
pfm1 = pfm;
info_convergence = 0;
switch ep.stochastic.ortpol
case 'hermite'
for u=1:ep.stochastic.order
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;
end
otherwise
error('extended_path:: Unknown orthogonal polynomial option!')
end
if ep.stochastic.order && ep.stochastic.scramble
exo_simul_1(2+ep.stochastic.order+1:2+ep.stochastic.order+ep.stochastic.scramble,positive_var_indx) = ...
randn(ep.stochastic.scramble,effective_number_of_shocks)*covariance_matrix_upper_cholesky;
end
if ep.init% Compute first order solution (Perturbation)...
ex = zeros(size(endo_simul_1,2),size(exo_simul_1,2));
ex(1:size(exo_simul_1,1),:) = exo_simul_1;
exo_simul_1 = ex;
initial_path = simult_(initial_conditions,dr,exo_simul_1(2:end,:),1);
endo_simul_1(:,1:end-1) = initial_path(:,1:end-1)*ep.init+endo_simul_1(:,1:end-1)*(1-ep.init);
else
endo_simul_1 = repmat(steady_state,1,periods1+2);
end
% Solve a perfect foresight model.
increase_periods = 0;
endo_simul = endo_simul_1;
while 1
if ~increase_periods
if bytecode_flag
[flag,tmp] = bytecode('dynamic');
else
flag = 1;
end
if flag
[flag,tmp] = solve_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm1);
end
info_convergence = ~flag;
end
if verbosity
if info_convergence
if t<10
disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
elseif t<100
disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
elseif t<1000
disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
else
disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
end
else
if t<10
disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
elseif t<100
disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
elseif t<1000
disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
else
disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
end
end
end
if do_not_check_stability_flag
% Exit from the while loop.
endo_simul_1 = tmp;
break
else
% Test if periods is big enough.
% Increase the number of periods.
periods1 = periods1 + ep.step;
pfm1.periods = periods1;
pfm1.i_upd = pfm1.ny+(1:pfm1.periods*pfm1.ny);
% Increment the counter.
increase_periods = increase_periods + 1;
if verbosity
if t<10
disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(periods1) '.'])
elseif t<100
disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(periods1) '.'])
elseif t<1000
disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(periods1) '.'])
else
disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(periods1) '.'])
end
end
if info_convergence
% If the previous call to the perfect foresight model solver exited
% announcing that the routine converged, adapt the size of endo_simul_1
% and exo_simul_1.
endo_simul_1 = [ tmp , repmat(steady_state,1,ep.step) ];
exo_simul_1 = [ exo_simul_1 ; zeros(ep.step,size(shocks,2)) ];
tmp_old = tmp;
else
% If the previous call to the perfect foresight model solver exited
% announcing that the routine did not converge, then tmp=1... Maybe
% should change that, because in some circonstances it may usefull
% to know where the routine did stop, even if convergence was not
% achieved.
endo_simul_1 = [ endo_simul_1 , repmat(steady_state,1,ep.step) ];
exo_simul_1 = [ exo_simul_1 ; zeros(ep.step,size(shocks,2)) ];
end
% Solve the perfect foresight model with an increased number of periods.
if bytecode_flag
[flag,tmp] = bytecode('dynamic');
else
flag = 1;
end
if flag
[flag,tmp] = solve_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm1);
end
info_convergence = ~flag;
if info_convergence
% If the solver achieved convergence, check that simulated paths did not
% change during the first periods.
% Compute the maximum deviation between old path and new path over the
% first periods
delta = max(max(abs(tmp(:,2:ep.fp)-tmp_old(:,2:ep.fp))));
if delta < dynatol.x
% If the maximum deviation is close enough to zero, reset the number
% of periods to ep.periods
periods1 = ep.periods;
pfm1.periods = periods1;
pfm1.i_upd = pfm1.ny+(1:pfm1.periods*pfm1.ny);
% Cut exo_simul_1 and endo_simul_1 consistently with the resetted
% number of periods and exit from the while loop.
exo_simul_1 = exo_simul_1(1:(periods1+2),:);
endo_simul_1 = endo_simul_1(:,1:(periods1+2));
break
end
else
% The solver did not converge... Try to solve the model again with a bigger
% number of periods, except if the number of periods has been increased more
% than 10 times.
if increase_periods==10;
if verbosity
if t<10
disp(['Time: ' int2str(t) '. Even with ' int2str(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
elseif t<100
disp(['Time: ' int2str(t) '. Even with ' int2str(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
elseif t<1000
disp(['Time: ' int2str(t) '. Even with ' int2str(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
else
disp(['Time: ' int2str(t) '. Even with ' int2str(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
end
end
% Exit from the while loop.
break
end
end% if info_convergence
end
end% while
if ~info_convergence% If exited from the while loop without achieving convergence, use an homotopic approach
[INFO,tmp] = homotopic_steps(.5,.01,pfm1);
if (~isstruct(INFO) && isnan(INFO)) || ~info_convergence
[INFO,tmp] = homotopic_steps(0,.01,pfm1);
if ~info_convergence
disp('Homotopy:: No convergence of the perfect foresight model solver!')
error('I am not able to simulate this model!');
else
info_convergence = 1;
endo_simul_1 = tmp;
if verbosity && info_convergence
disp('Homotopy:: Convergence of the perfect foresight model solver!')
end
end
else
info_convergence = 1;
endo_simul_1 = tmp;
if verbosity && info_convergence
disp('Homotopy:: Convergence of the perfect foresight model solver!')
end
end
end
% Save results of the perfect foresight model solver.
if ep.memory
mArray1(:,:,s,t) = endo_simul_1(:,1:100);
mArrat2(:,:,s,t) = transpose(exo_simul_1(1:100,:));
end
results(:,s) = www(s)*endo_simul_1(:,2);
end
time_series(:,t) = sum(results,2);
oo_.endo_simul(:,1:end-1) = oo_.endo_simul(:,2:end);
oo_.endo_simul(:,1) = time_series(:,t);
oo_.endo_simul(:,end) = oo_.steady_state;
end% (while) loop over t
dyn_waitbar_close(hh);
oo_.endo_simul = oo_.steady_state;
if ep.memory
save([M_.fname '_memory'],'mArray1','mArray2','www');
end
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