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Try first Ferhat's code and if it fails try the matlab's implementation of the the perfect foresight model solver.

time-shift
Stéphane Adjemian (Charybdis) 2012-02-03 12:39:48 +01:00
parent ac54f7f528
commit fed2e229e5
1 changed files with 39 additions and 52 deletions
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91
matlab/ep/extended_path.m
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@ -35,40 +35,35 @@ global M_ options_ oo_
options_.verbosity = options_.ep.verbosity;
verbosity = options_.ep.verbosity+options_.ep.debug;
% Test if bytecode and block options are used (these options are mandatory)
if ~( options_.bytecode && options_.block )
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;
use_solve_perfect_foresight_models_routine = 1;
else
use_solve_perfect_foresight_models_routine = 0;
end
% 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;
% Set default initial conditions.
if isempty(initial_conditions)
@ -80,10 +75,8 @@ options_.maxit_ = options_.ep.maxit;
% Set the number of periods for the perfect foresight model
options_.periods = options_.ep.periods;
if use_solve_perfect_foresight_models_routine
pfm.periods = options_.ep.periods;
pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
end
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;
@ -226,10 +219,9 @@ while (t<sample_size)
endo_simul = oo_.endo_simul;
while 1
if ~increase_periods
if use_solve_perfect_foresight_models_routine
[flag,tmp] = bytecode('dynamic');
if flag
[flag,tmp] = solve_perfect_foresight_model(oo_.endo_simul,oo_.exo_simul,pfm);
else
[flag,tmp] = bytecode('dynamic');
end
info.convergence = ~flag;
end
@ -266,10 +258,8 @@ while (t<sample_size)
else
% Increase the number of periods.
options_.periods = options_.periods + options_.ep.step;
if use_solve_perfect_foresight_models_routine
pfm.periods = options_.periods;
pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
end
pfm.periods = options_.periods;
pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
% Increment the counter.
increase_periods = increase_periods + 1;
if verbosity
@ -300,10 +290,9 @@ while (t<sample_size)
oo_.exo_simul = [ oo_.exo_simul ; zeros(options_.ep.step,size(shocks,2)) ];
end
% Solve the perfect foresight model with an increased number of periods.
if use_solve_perfect_foresight_models_routine
[flag,tmp] = bytecode('dynamic');
if flag
[flag,tmp] = solve_perfect_foresight_model(oo_.endo_simul,oo_.exo_simul,pfm);
else
[flag,tmp] = bytecode('dynamic');
end
info.convergence = ~flag;
if info.convergence
@ -316,10 +305,8 @@ while (t<sample_size)
% If the maximum deviation is close enough to zero, reset the number
% of periods to ep.periods
options_.periods = options_.ep.periods;
if use_solve_perfect_foresight_models_routine
pfm.periods = options_.periods;
pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
end
pfm.periods = options_.periods;
pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
% Cut oo_.exo_simul and oo_.endo_simul consistently with the resetted
% number of periods and exit from the while loop.
oo_.exo_simul = oo_.exo_simul(1:(options_.periods+2),:);