simplified extended_path.m.
homotopy and variable sample length should be handled in the solvers.time-shift
Michel Juillard
parent
da8f702429
commit
5ff129e52d
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@ -42,7 +42,6 @@ options_.simul.maxit = options_.ep.maxit;
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pfm = setup_stochastic_perfect_foresight_model_solver(M_,options_,oo_);
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exo_nbr = M_.exo_nbr;
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periods = options_.periods;
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ep = options_.ep;
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steady_state = oo_.steady_state;
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dynatol = options_.dynatol;
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@ -86,6 +85,8 @@ end
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options_.minimal_solving_period = 100;%options_.ep.periods;
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% Initialize the exogenous variables.
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% !!!!!!!! Needs to fixed
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options_.periods = periods;
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make_ex_;
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% Initialize the endogenous variables.
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@ -116,7 +117,7 @@ bytecode_flag = options_.ep.use_bytecode;
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% Simulate shocks.
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switch options_.ep.innovation_distribution
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case 'gaussian'
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oo_.ep.shocks = transpose(transpose(covariance_matrix_upper_cholesky)*randn(effective_number_of_shocks,sample_size));
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oo_.ep.shocks = transpose(transpose(covariance_matrix_upper_cholesky)*randn(effective_number_of_shocks,sample_size));
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otherwise
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error(['extended_path:: ' options_.ep.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!'])
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end
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@ -148,7 +149,7 @@ if options_.ep.stochastic.order > 0
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pfm.nnodes = nnodes;
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% compute number of blocks
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[block_nbr,pfm.world_nbr] = get_block_world_nbr(options_.ep.stochastic.algo,nnodes,options_.ep.ut.k,options_.ep.periods);
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[block_nbr,pfm.world_nbr] = get_block_world_nbr(options_.ep.stochastic.algo,nnodes,options_.ep.stochastic.order,options_.ep.periods);
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else
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block_nbr = options_.ep.periods
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end
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@ -175,217 +176,51 @@ while (t<sample_size)
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end
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% Set period index.
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t = t+1;
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shocks = oo_.ep.shocks(t,:);
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% Put it in oo_.exo_simul (second line).
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oo_.exo_simul(2,positive_var_indx) = shocks;
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periods1 = periods;
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exo_simul_1 = zeros(periods1+2,exo_nbr);
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exo_simul_1(2,:) = oo_.exo_simul(2,:);
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pfm1 = pfm;
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info_convergence = 0;
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% Put shocks in oo_.exo_simul (second line).
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exo_simul_1 = zeros(periods+2,exo_nbr);
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exo_simul_1(2,positive_var_indx) = oo_.exo_simul(2,positive_var_indx) + oo_.ep.shocks(t,:);
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if ep.init% Compute first order solution (Perturbation)...
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ex = zeros(size(endo_simul_1,2),size(exo_simul_1,2));
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ex(1:size(exo_simul_1,1),:) = exo_simul_1;
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exo_simul_1 = ex;
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initial_path = simult_(initial_conditions,dr,exo_simul_1(2:end,:),1);
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endo_simul_1(:,1:end-1) = initial_path(:,1:end-1)*ep.init+endo_simul_1(:,1:end-1)*(1-ep.init);
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else
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if t==1
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endo_simul_1 = repmat(steady_state,1,periods1+2);
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endo_simul_1 = repmat(steady_state,1,periods+2);
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end
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end
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% Solve a perfect foresight model.
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increase_periods = 0;
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% Keep a copy of endo_simul_1
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endo_simul = endo_simul_1;
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if verbosity
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save ep_test_1 endo_simul_1 exo_simul_1
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end
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while 1
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if ~increase_periods
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if bytecode_flag && ~options_.ep.stochastic.order
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[flag,tmp] = bytecode('dynamic',endo_simul_1,exo_simul_1, M_.params, endo_simul_1, options_.ep.periods);
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else
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flag = 1;
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end
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if flag
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if options_.ep.stochastic.order == 0
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[flag,tmp,err] = solve_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm1);
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else
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switch(options_.ep.stochastic.algo)
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case 0
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[flag,tmp] = ...
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solve_stochastic_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm1,options_.ep.stochastic.quadrature.nodes,options_.ep.stochastic.order);
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case 1
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[flag,tmp] = ...
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solve_stochastic_perfect_foresight_model_1(endo_simul_1,exo_simul_1,options_,pfm1,options_.ep.stochastic.order);
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end
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end
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end
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info_convergence = ~flag;
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end
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if verbosity
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if info_convergence
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if t<10
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disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
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elseif t<100
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disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
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elseif t<1000
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disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
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else
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disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
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end
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else
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if t<10
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disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
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elseif t<100
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disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
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elseif t<1000
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disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
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else
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disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
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end
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end
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end
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if do_not_check_stability_flag
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% Exit from the while loop.
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endo_simul_1 = tmp;
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break
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if bytecode_flag && ~options_.ep.stochastic.order
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[flag,tmp] = bytecode('dynamic',endo_simul_1,exo_simul_1, M_.params, endo_simul_1, options_.ep.periods);
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else
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flag = 1;
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end
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if flag
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if options_.ep.stochastic.order == 0
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[flag,tmp,err] = solve_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm);
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else
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% Test if periods is big enough.
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% Increase the number of periods.
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periods1 = periods1 + ep.step;
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pfm1.periods = periods1;
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pfm1.i_upd = pfm1.ny+(1:pfm1.periods*pfm1.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(periods1) '.'])
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elseif t<100
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disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(periods1) '.'])
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elseif t<1000
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disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(periods1) '.'])
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else
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disp(['Time: ' int2str(t) '. I increase the number of periods to ' int2str(periods1) '.'])
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end
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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 endo_simul_1
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% and exo_simul_1.
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endo_simul_1 = [ tmp , repmat(steady_state,1,ep.step) ];
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exo_simul_1 = [ exo_simul_1 ; zeros(ep.step,exo_nbr)];
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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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endo_simul_1 = [ endo_simul_1 , repmat(steady_state,1,ep.step) ];
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exo_simul_1 = [ exo_simul_1 ; zeros(ep.step,exo_nbr)];
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end
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% Solve the perfect foresight model with an increased number of periods.
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if bytecode_flag && ~options_.ep.stochastic.order
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[flag,tmp] = bytecode('dynamic',endo_simul_1,exo_simul_1, M_.params, endo_simul_1, options_.ep.periods);
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else
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flag = 1;
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end
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if flag
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if options_.ep.stochastic.order == 0
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[flag,tmp,err] = solve_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm1);
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else
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switch(options_.ep.stochastic.algo)
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case 0
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[flag,tmp] = ...
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solve_stochastic_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm1,options_.ep.stochastic.quadrature.nodes,options_.ep.stochastic.order);
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case 1
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[flag,tmp] = ...
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solve_stochastic_perfect_foresight_model_1(endo_simul_1,exo_simul_1,options_,pfm1,options_.ep.stochastic.order);
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end
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end
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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)-tmp_old(:,2))));
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if delta < 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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periods1 = ep.periods;
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pfm1.periods = periods1;
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pfm1.i_upd = pfm1.ny+(1:pfm1.periods*pfm1.ny);
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% Cut exo_simul_1 and endo_simul_1 consistently with the resetted
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% number of periods and exit from the while loop.
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exo_simul_1 = exo_simul_1(1:(periods1+2),:);
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endo_simul_1 = endo_simul_1(:,1:(periods1+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(periods1) ', 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(periods1) ', 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(periods1) ', 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(periods1) ', I am not able to solve the perfect foresight model. Use homotopy instead...'])
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end
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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
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end% while
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if ~info_convergence && ep.homotopic_steps % If exited from the while loop without achieving
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% convergence use an homotopic approach
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if ~do_not_check_stability_flag
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periods1 = ep.periods;
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pfm1.periods = periods1;
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pfm1.i_upd = i_upd;
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exo_simul_1 = exo_simul_1(1:(periods1+2),:);
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endo_simul_1 = endo_simul_1(:,1:(periods1+2));
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end
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[INFO,tmp] = homotopic_steps(endo_simul,exo_simul_1,.5,.01,pfm1);
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if isstruct(INFO)
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info_convergence = INFO.convergence;
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else
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info_convergence = 0;
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end
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if ~info_convergence
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[INFO,tmp] = homotopic_steps(endo_simul,exo_simul_1,0,.01,pfm1);
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if isstruct(INFO)
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info_convergence = INFO.convergence;
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else
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info_convergence = 0;
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end
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if ~info_convergence
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disp('Homotopy:: No convergence of the perfect foresight model solver!')
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error('I am not able to simulate this model!');
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else
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endo_simul_1 = tmp;
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if verbosity && info_convergence
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disp('Homotopy:: Convergence of the perfect foresight model solver!')
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end
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end
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else
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info_convergence = 1;
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endo_simul_1 = tmp;
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if verbosity && info_convergence
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disp('Homotopy:: Convergence of the perfect foresight model solver!')
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switch(options_.ep.stochastic.algo)
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case 0
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[flag,tmp] = ...
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solve_stochastic_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm,options_.ep.stochastic.quadrature.nodes,options_.ep.stochastic.order);
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case 1
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[flag,tmp] = ...
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solve_stochastic_perfect_foresight_model_1(endo_simul_1,exo_simul_1,options_,pfm,options_.ep.stochastic.order);
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end
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end
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end
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info_convergence = ~flag;
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if verbosity
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if info_convergence
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disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
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else
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disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
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end
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end
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endo_simul_1 = tmp;
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if info_convergence
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% Save results of the perfect foresight model solver.
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time_series(:,tsimul) = endo_simul_1(:,2);
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@ -397,7 +232,7 @@ while (t<sample_size)
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oo_.ep.failures.periods = [oo_.ep.failures.periods t];
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oo_.ep.failures.previous_period = [oo_.ep.failures.previous_period endo_simul_1(:,1)];
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oo_.ep.failures.shocks = [oo_.ep.failures.shocks shocks];
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endo_simul_1 = repmat(steady_state,1,periods1+2);
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endo_simul_1 = repmat(steady_state,1,periods+2);
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endo_simul_1(:,1) = time_series(:,tsimul-1);
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end
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end% (while) loop over t
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