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Fixed bug in homotopy. 

Convex combination for initial/terminal endogenous variables paths was wrong.
time-shift
Stéphane Adjemian(Charybdis) 2016-03-24 22:42:44 +01:00
parent fd850ca5bd
commit e714dc1a9f
1 changed files with 35 additions and 27 deletions
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62
matlab/perfect-foresight-models/perfect_foresight_solver.m
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@ -1,18 +1,18 @@
function perfect_foresight_solver()
% Computes deterministic simulations
%
%
% INPUTS
% None
%
%
% OUTPUTS
% none
%
%
% ALGORITHM
%
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 1996-2015 Dynare Team
% Copyright (C) 1996-2016 Dynare Team
%
% This file is part of Dynare.
%
@ -45,12 +45,12 @@ if options_.debug
[residual(:,ii)] = model_static(oo_.steady_state, oo_.exo_simul(ii,:),M_.params);
end
problematic_periods=find(any(isinf(residual)) | any(isnan(residual)))-M_.maximum_endo_lag;
if ~isempty(problematic_periods)
if ~isempty(problematic_periods)
period_string=num2str(problematic_periods(1));
for ii=2:length(problematic_periods)
period_string=[period_string, ', ', num2str(problematic_periods(ii))];
end
fprintf('\n\nWARNING: Value for the exogenous variable(s) in period(s) %s inconsistent with the static model.\n',period_string);
fprintf('\n\nWARNING: Value for the exogenous variable(s) in period(s) %s inconsistent with the static model.\n',period_string);
fprintf('WARNING: Check for division by 0.\n')
end
end
@ -62,15 +62,17 @@ oo_ = perfect_foresight_solver_core(M_,options_,oo_);
% If simulation failed try homotopy.
if ~oo_.deterministic_simulation.status && ~options_.no_homotopy
skipline()
disp('Simulation of the perfect foresight model failed!')
disp('Switching to a homotopy method...')
skipline()
if ~M_.maximum_lag
disp('Homotopy not implemented for purely forward models!')
disp('Failed to solve the model!')
disp('Return with empty oo_.endo_simul.')
oo_.endo_simul = [];
skipline()
return
end
if ~M_.maximum_lead
@ -78,25 +80,26 @@ if ~oo_.deterministic_simulation.status && ~options_.no_homotopy
disp('Failed to solve the model!')
disp('Return with empty oo_.endo_simul.')
oo_.endo_simul = [];
skipline()
return
end
skipline()
% Disable warnings if homotopy
warning_old_state = warning;
warning off all
% Do not print anything
oldverbositylevel = options_.verbosity;
options_.verbosity = 0;
exosim = oo_.exo_simul;
exoinit = repmat(oo_.exo_steady_state',M_.maximum_lag+options_.periods+M_.maximum_lead,1);
endosim = oo_.endo_simul;
endoinit = repmat(oo_.steady_state, 1,M_.maximum_lag+options_.periods+M_.maximum_lead);
current_weight = 0; % Current weight of target point in convex combination
step = .5;
% Set initial paths for the endogenous and exogenous variables.
endoinit = repmat(oo_.steady_state, 1,M_.maximum_lag+options_.periods+M_.maximum_lead);
exoinit = repmat(oo_.exo_steady_state',M_.maximum_lag+options_.periods+M_.maximum_lead,1);
% Copy the current paths for the exogenous and endogenous variables.
exosim = oo_.exo_simul;
endosim = oo_.endo_simul;
current_weight = 0; % Current weight of target point in convex combination.
step = .5; % Set default step size.
success_counter = 0;
iteration = 0;
@ -120,21 +123,24 @@ if ~oo_.deterministic_simulation.status && ~options_.no_homotopy
% Compute convex combination for exo path and initial/terminal endo conditions
% But take care of not overwriting the computed part of oo_.endo_simul
oo_.exo_simul = exosim*new_weight + exoinit*(1-new_weight);
oo_.endo_simul(:,[initperiods, lastperiods]) = ...
new_weight*oo_.endo_simul(:,[initperiods, lastperiods])+(1-new_weight)*endoinit(:,[initperiods, lastperiods]);
oo_.endo_simul(:,[initperiods, lastperiods]) = new_weight*endosim(:,[initperiods, lastperiods])+(1-new_weight)*endoinit(:,[initperiods, lastperiods]);
% Detect Nans or complex numbers in the solution.
path_with_nans = any(any(isnan(oo_.endo_simul)));
path_with_cplx = any(any(~isreal(oo_.endo_simul)));
if isequal(iteration,1)
if isequal(iteration, 1)
% First iteration, same initial guess as in the first call to perfect_foresight_solver_core routine.
oo_.endo_simul(:,M_.maximum_lag+1:end-M_.maximum_lead) = endoinit(:,1:options_.periods);
elseif path_with_nans || path_with_cplx
% If solver failed with NaNs or complex number, use previous solution as an initial guess.
oo_.endo_simul(:,M_.maximum_lag+1:end-M_.maximum_lead) = saved_endo_simul(:,1+M_.maximum_lag:end-M_.maximum_lead);
end
% Make a copy of the paths.
saved_endo_simul = oo_.endo_simul;
% Solve for the paths of the endogenous variables.
[oo_,me] = perfect_foresight_solver_core(M_,options_,oo_);
if oo_.deterministic_simulation.status == 1
@ -150,6 +156,7 @@ if ~oo_.deterministic_simulation.status && ~options_.no_homotopy
end
fprintf('%i \t | %1.5f \t | %s \t | %e\n', iteration, new_weight, 'succeeded', me)
else
% If solver failed, then go back.
oo_.endo_simul = saved_endo_simul;
success_counter = 0;
step = step / 2;
@ -168,11 +175,12 @@ end
if oo_.deterministic_simulation.status == 1
disp('Perfect foresight solution found.')
skipline()
else
warning('Failed to solve perfect foresight model')
end
skipline()
dyn2vec;
if ~isdates(options_.initial_period) && isnan(options_.initial_period)
@ -182,4 +190,4 @@ else
end
ts = dseries(transpose(oo_.endo_simul),initial_period,cellstr(M_.endo_names));
assignin('base', 'Simulated_time_series', ts);
assignin('base', 'Simulated_time_series', ts);