Removed stability test over the last periods of the perfect foresight solution.
Stéphane Adjemian (Charybdis)
parent
64ebd1d0d7
commit
43f46f2886
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@ -248,94 +248,84 @@ while (t<sample_size)
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end
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end
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end
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% Test if periods is big enough. The variable delta measures the maximum absolute variation during
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% the last periods of the simulated path. This variation has to be close to zero (because the
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% economy is assumed to be in the steady state at the end of the simulated path).
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if info.convergence
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delta = max(max(abs(tmp(:,end-options_.ep.lp:end)-tmp(:,end-options_.ep.lp-1:end-1))));
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% Test if periods is big enough.
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% Increase the number of periods.
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options_.periods = options_.periods + options_.ep.step;
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pfm.periods = options_.periods;
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pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.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(options_.periods) '.'])
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elseif t<100
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disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(options_.periods) '.'])
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elseif t<1000
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disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(options_.periods) '.'])
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else
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disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(options_.periods) '.'])
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end
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end
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if info.convergence && ~increase_periods && delta<options_.dynatol.x
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% Exit from the while loop (the number of periods is big enough).
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break
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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 oo_.endo_simul
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% and oo_.exo_simul.
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oo_.endo_simul = [ tmp , repmat(oo_.steady_state,1,options_.ep.step) ];
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oo_.exo_simul = [ oo_.exo_simul ; zeros(options_.ep.step,size(shocks,2)) ];
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tmp_old = tmp;
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else
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% Increase the number of periods.
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options_.periods = options_.periods + options_.ep.step;
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pfm.periods = options_.periods;
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pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.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(options_.periods) '.'])
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elseif t<100
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disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(options_.periods) '.'])
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elseif t<1000
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disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(options_.periods) '.'])
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else
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disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(options_.periods) '.'])
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end
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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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oo_.endo_simul = [ oo_.endo_simul , repmat(oo_.steady_state,1,options_.ep.step) ];
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oo_.exo_simul = [ oo_.exo_simul ; zeros(options_.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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[flag,tmp] = bytecode('dynamic');
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if flag
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[flag,tmp] = solve_perfect_foresight_model(oo_.endo_simul,oo_.exo_simul,pfm);
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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:options_.ep.fp)-tmp_old(:,2:options_.ep.fp))));
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if delta<options_.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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options_.periods = options_.ep.periods;
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pfm.periods = options_.periods;
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pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
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% Cut oo_.exo_simul and oo_.endo_simul consistently with the resetted
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% number of periods and exit from the while loop.
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oo_.exo_simul = oo_.exo_simul(1:(options_.periods+2),:);
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oo_.endo_simul = oo_.endo_simul(:,1:(options_.periods+2));
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break
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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 oo_.endo_simul
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% and oo_.exo_simul.
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oo_.endo_simul = [ tmp , repmat(oo_.steady_state,1,options_.ep.step) ];
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oo_.exo_simul = [ oo_.exo_simul ; zeros(options_.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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oo_.endo_simul = [ oo_.endo_simul , repmat(oo_.steady_state,1,options_.ep.step) ];
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oo_.exo_simul = [ oo_.exo_simul ; zeros(options_.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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[flag,tmp] = bytecode('dynamic');
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if flag
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[flag,tmp] = solve_perfect_foresight_model(oo_.endo_simul,oo_.exo_simul,pfm);
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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:options_.ep.fp)-tmp_old(:,2:options_.ep.fp))));
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if delta<options_.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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options_.periods = options_.ep.periods;
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pfm.periods = options_.periods;
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pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
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% Cut oo_.exo_simul and oo_.endo_simul consistently with the resetted
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% number of periods and exit from the while loop.
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oo_.exo_simul = oo_.exo_simul(1:(options_.periods+2),:);
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oo_.endo_simul = oo_.endo_simul(:,1:(options_.periods+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(options_.periods) ', 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(options_.periods) ', 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(options_.periods) ', 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(options_.periods) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
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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(options_.periods) ', 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(options_.periods) ', 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(options_.periods) ', 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(options_.periods) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
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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% ~increase_periods && delta<options_.dynatol.x
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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% 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);
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