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Fixed bugs in routines related to the extended path algorithm.

time-shift
Stéphane Adjemian (Charybdis) 2012-03-02 11:33:29 +01:00
parent 0aaa7a8a13
commit 192b5c7113
3 changed files with 157 additions and 132 deletions
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48
matlab/ep/extended_path.m
View File

@ -105,6 +105,9 @@ periods = options_.ep.periods;
pfm.periods = options_.ep.periods;
pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
% keep a copy of pfm.i_upd
i_upd = pfm.i_upd;
% Set the algorithm for the perfect foresight solver
options_.stack_solve_algo = options_.ep.stack_solve_algo;
@ -190,23 +193,27 @@ while (t<sample_size)
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);
if t==1
endo_simul_1 = repmat(steady_state,1,periods1+2);
end
end
% Solve a perfect foresight model.
increase_periods = 0;
% Keep a copy of endo_simul_1
endo_simul = endo_simul_1;
while 1
if ~increase_periods
if bytecode_flag
oo_.endo_simul = endo_simul_1;
oo_.exo_simul = exo_simul_1;
[flag,tmp] = bytecode('dynamic');
flag
pause
else
flag = 1;
end
if flag
[flag,tmp] = solve_stochastic_perfect_foresight_model(endo_simul_1,exo_simul_1,...
pfm1, ...
options_.ep.nnodes,...
options_.ep.order);
[flag,tmp,err] = solve_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm1);
end
info_convergence = ~flag;
end
@ -275,6 +282,8 @@ while (t<sample_size)
end
% Solve the perfect foresight model with an increased number of periods.
if bytecode_flag
oo_.endo_simul = endo_simul_1;
oo_.exo_simul = exo_simul_1;
[flag,tmp] = bytecode('dynamic');
else
flag = 1;
@ -327,14 +336,30 @@ while (t<sample_size)
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 ~do_not_check_stability_flag
periods1 = ep.periods;
pfm1.periods = periods1;
pfm1.i_upd = i_upd;
exo_simul_1 = exo_simul_1(1:(periods1+2),:);
endo_simul_1 = endo_simul_1(:,1:(periods1+2));
end
[INFO,tmp] = homotopic_steps(endo_simul,exo_simul_1,.5,.01,pfm);
if isstruct(INFO)
info_convergence = INFO.convergence;
else
info_convergence = 0;
end
if ~info_convergence
[INFO,tmp] = homotopic_steps(endo_simul,exo_simul_1,0,.01,pfm);
if isstruct(INFO)
info_convergence = INFO.convergence;
else
info_convergence = 0;
end
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!')
@ -349,7 +374,10 @@ while (t<sample_size)
end
end
% Save results of the perfect foresight model solver.
time_series(:,t) = tmp;
time_series(:,t) = endo_simul_1(:,2);
endo_simul_1(:,1:end-1) = endo_simul_1(:,2:end);
endo_simul_1(:,1) = time_series(:,t);
endo_simul_1(:,end) = oo_.steady_state;
end% (while) loop over t
dyn_waitbar_close(hh);

225
matlab/ep/homotopic_steps.m
View File

@ -1,5 +1,5 @@
function [info,tmp] = homotopic_steps(initial_weight,step_length,pfm)
global oo_ options_ M_
function [info,tmp] = homotopic_steps(endo_simul0,exo_simul0,initial_weight,step_length,pfm)
global options_ oo_
%Set bytecode flag
bytecode_flag = options_.ep.use_bytecode;
@ -9,26 +9,8 @@ increase_factor = 5.0;
decrease_factor = 0.2;
% Save current state of oo_.endo_simul and oo_.exo_simul.
endo_simul = oo_.endo_simul;
exxo_simul = oo_.exo_simul;
[idx,jdx] = find(abs(exxo_simul)>1e-12);
idx = unique(idx);
if idx(1)-2
% The first scalar in idx must be equal to two.
error('extended_path::homotopy: Something is wrong in oo_.exo_simul!')
end
stochastic_extended_path_depth = length(idx);
if stochastic_extended_path_depth>1
for i=2:length(idx)
if (idx(i)-idx(i-1)-1)
error('extended_path::homotopy: Something is wrong in oo_.exo_simul!')
end
end
end
endo_simul = endo_simul0;
exxo_simul = exo_simul0;
initial_step_length = step_length;
max_iter = 1000/step_length;
@ -41,115 +23,114 @@ if verbose
format long
end
for d=1:stochastic_extended_path_depth
% (re)Set iter.
iter = 0;
% (re)Set iter.
jter = 0;
% (re)Set weight.
weight = initial_weight;
% (re)Set exo_simul to zero.
oo_.exo_simul = zeros(size(oo_.exo_simul));
while weight<1
iter = iter+1;
oo_.exo_simul(idx(d),:) = weight*exxo_simul(idx(d),:);
if d>1
oo_.exo_simul(1:idx(d-1),:) = exxo_simul(1:idx(d-1),:);
end
if bytecode_flag
[flag,tmp] = bytecode('dynamic');
else
flag = 1;
end
if flag
[flag,tmp] = solve_perfect_foresight_model(oo_.endo_simul,oo_.exo_simul,pfm);
end
info.convergence = ~flag;% Equal to one if the perfect foresight solver converged for the current value of weight.
if verbose
if info.convergence
disp(['Iteration n° ' int2str(iter) ', weight is ' num2str(weight,8) ', Ok!' ])
else
disp(['Iteration n° ' int2str(iter) ', weight is ' num2str(weight,8) ', Convergence problem!' ])
end
end
% (re)Set iter.
iter = 0;
% (re)Set iter.
jter = 0;
% (re)Set weight.
weight = initial_weight;
% (re)Set exo_simul to zero.
exo_simul0 = zeros(size(exo_simul0));
while weight<1
iter = iter+1;
exo_simul0(2,:) = weight*exxo_simul(2,:);
if bytecode_flag
oo_.endo_simul = endo_simul_1;
oo_.exo_simul = exo_simul_1;
[flag,tmp] = bytecode('dynamic');
else
flag = 1;
end
if flag
[flag,tmp] = solve_perfect_foresight_model(endo_simul0,exo_simul0,pfm);
end
info.convergence = ~flag;% Equal to one if the perfect foresight solver converged for the current value of weight.
if verbose
if info.convergence
if d<stochastic_extended_path_depth
oo_.endo_simul = tmp;
end
jter = jter + 1;
if jter>3
if verbose
disp('I am increasing the step length!')
end
step_length=step_length*increase_factor;
jter = 0;
end
if abs(1-weight)<options_.dynatol.x;
break
end
weight = weight+step_length;
else% Perfect foresight solver failed for the current value of weight.
if initial_weight>0 && abs(weight-initial_weight)<1e-12% First iteration, the initial weight is too high.
if verbose
disp('I am reducing the initial weight!')
end
initial_weight = initial_weight/2;
weight = initial_weight;
if weight<1e-12
oo_.endo_simul = endo_simul;
oo_.exo_simul = exxo_simul;
info.convergence = 0;
info.depth = d;
tmp = [];
return
end
continue
else% Initial weight is OK, but the perfect foresight solver failed on some subsequent iteration.
if verbose
disp('I am reducing the step length!')
end
jter = 0;
if weight>0
weight = weight-step_length;
end
step_length=step_length*decrease_factor;
weight = weight+step_length;
if step_length<options_.dynatol.x
break
end
continue
end
end
if iter>max_iter
info = NaN;
return
disp(['Iteration n° ' int2str(iter) ', weight is ' num2str(weight,8) ', Ok!' ])
else
disp(['Iteration n° ' int2str(iter) ', weight is ' num2str(weight,8) ', Convergence problem!' ])
end
end
if weight<1
oo_.exo_simul = exxo_simul;
if bytecode_flag
[flag,tmp] = bytecode('dynamic');
else
flag = 1;
if info.convergence
%if d<stochastic_extended_path_depth
endo_simul0 = tmp;
%end
jter = jter + 1;
if jter>3
if verbose
disp('I am increasing the step length!')
end
step_length=step_length*increase_factor;
jter = 0;
end
if flag
[flag,tmp] = solve_perfect_foresight_model(oo_.endo_simul,oo_.exo_simul,pfm);
if abs(1-weight)<options_.dynatol.x;
break
end
info.convergence = ~flag;
if info.convergence
oo_.endo_simul = tmp;
return
else
if step_length>options_.dynatol.x
oo_.endo_simul = endo_simul;
oo_.exo_simul = exxo_simul;
weight = weight+step_length;
else% Perfect foresight solver failed for the current value of weight.
if initial_weight>0 && abs(weight-initial_weight)<1e-12% First iteration, the initial weight is too high.
if verbose
disp('I am reducing the initial weight!')
end
initial_weight = initial_weight/2;
weight = initial_weight;
if weight<1e-12
endo_simul0 = endo_simul;
exo_simul0 = exxo_simul;
info.convergence = 0;
info.depth = d;
tmp = [];
return
else
error('extended_path::homotopy: Oups! I did my best, but I am not able to simulate this model...')
end
continue
else% Initial weight is OK, but the perfect foresight solver failed on some subsequent iteration.
if verbose
disp('I am reducing the step length!')
end
jter = 0;
if weight>0
weight = weight-step_length;
end
step_length=step_length*decrease_factor;
weight = weight+step_length;
if step_length<options_.dynatol.x
break
end
continue
end
end
end
if iter>max_iter
info = NaN;
return
end
end
if weight<1
exo_simul0 = exxo_simul;
if bytecode_flag
oo_.endo_simul = endo_simul_1;
oo_.exo_simul = exo_simul_1;
[flag,tmp] = bytecode('dynamic');
else
flag = 1;
end
if flag
[flag,tmp] = solve_perfect_foresight_model(endo_simul0,exo_simul0,pfm);
end
info.convergence = ~flag;
if info.convergence
endo_simul0 = tmp;
return
else
if step_length>options_.dynatol.x
endo_simul0 = endo_simul;
exo_simul0 = exxo_simul;
info.convergence = 0;
info.depth = d;
tmp = [];
return
else
error('extended_path::homotopy: Oups! I did my best, but I am not able to simulate this model...')
end
end
end

16
matlab/solve_perfect_foresight_model.m
View File

@ -3,6 +3,7 @@ function [flag,endo_simul,err] = solve_perfect_foresight_model(endo_simul,exo_si
flag = 0;
err = 0;
stop = 0;
nan_flag = 0;
model_dynamic = pfm.dynamic_model;
@ -57,6 +58,10 @@ function [flag,endo_simul,err] = solve_perfect_foresight_model(endo_simul,exo_si
break
end
dy = -A\res;
if any(isnan(dy))
nan_flag = 1;
break
end
Y(pfm.i_upd) = Y(pfm.i_upd) + dy;
end
@ -71,6 +76,17 @@ function [flag,endo_simul,err] = solve_perfect_foresight_model(endo_simul,exo_si
flag = 1;% more iterations are needed.
endo_simul = 1;
end
if nan_flag
if pfm.verbose
fprintf('\n') ;
disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
fprintf('\n') ;
disp(['WARNING : NaNs!']) ;
fprintf('\n') ;
end
flag = 1;
endo_simul = 1;
end
if pfm.verbose
disp (['-----------------------------------------------------']) ;
end